Project

1.Get informtion of the datatypes 2.If there is any class data in the features,convert them into binary from 3.Look how ages of the data are looking and get some information 4.Get the descriptive statistics of the above data 5.Construct histogram, boxplot for every column using For loop 6.Get the correlation heatmap and find if there is association between certain columns 7.Choose the specific columns which have certain association and get the pairplot

#importing of main libriries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

C:\Users\vinayaka\anaconda3\lib\site-packages\scipy\__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.25.0 is required for this version of SciPy (detected version 1.26.4
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"

# import the data set
data = pd.read_csv("C:\\Users\\vinayaka\Downloads\\Minsk2020_ALS_dataset.csv")

#first five rows of data 
data.head()

	ID	Sex	Age	J1_a	J3_a	J5_a	J55_a	S1_a	S3_a	S5_a	...	dCCi(7)	dCCi(8)	dCCi(9)	dCCi(10)	dCCi(11)	dCCi(12)	d_1	F2_i	F2_{conv}	Diagnosis (ALS)
0	8	M	58	0.321817	0.141230	0.199128	0.923634	6.044559	3.196477	3.770575	...	-0.024467	-0.005300	0.051874	-0.037710	-0.026549	-0.021149	4.825476	2526.285657	833.498083	1
1	20	F	57	0.344026	0.177032	0.206458	0.827714	1.967728	0.856639	1.179851	...	0.002485	-0.004535	-0.000225	-0.006977	-0.012510	0.014773	5.729322	1985.712014	561.802625	1
2	21	F	58	0.264740	0.148228	0.177078	0.532566	1.850893	0.942743	1.071950	...	-0.013927	0.007908	0.007960	-0.009022	-0.012488	-0.015588	8.258488	2364.695972	796.723440	1
3	22	F	70	0.455793	0.174870	0.243660	0.962641	2.883768	1.284926	1.915058	...	-0.019285	-0.021768	0.020495	0.035976	-0.034648	0.008021	5.447137	1860.172768	359.409974	1
4	24	M	66	0.269335	0.143961	0.167465	0.547745	2.327924	1.164109	1.420891	...	-0.005743	0.004726	-0.015247	0.003900	-0.007686	-0.003784	8.562517	2051.627447	817.111847	1

5 rows × 135 columns

# last five rows of  data
data.tail()

	ID	Sex	Age	J1_a	J3_a	J5_a	J55_a	S1_a	S3_a	S5_a	...	dCCi(7)	dCCi(8)	dCCi(9)	dCCi(10)	dCCi(11)	dCCi(12)	d_1	F2_i	F2_{conv}	Diagnosis (ALS)
59	123	M	43	0.255799	0.123679	0.182658	0.505591	6.222031	2.876602	3.894294	...	0.220533	0.089766	-0.120838	-0.004221	-0.013165	0.004642	9.855665	3128.341308	1990.937097	0
60	125	M	63	0.513175	0.296489	0.334845	0.729804	9.686563	4.327943	5.687977	...	0.028016	-0.038739	0.011588	-0.011281	-0.004294	0.011239	11.094558	1964.218942	601.076046	0
61	127	F	67	0.383901	0.245923	0.251359	0.415136	4.148414	2.069757	2.527213	...	0.011685	0.007883	-0.014839	0.013859	0.011145	0.001418	12.564742	2526.285657	934.343638	0
62	129	F	68	1.336216	0.815757	0.733197	0.981928	11.224542	5.295879	6.994751	...	0.015712	0.013437	0.025113	0.008852	-0.010132	-0.008458	10.670669	3201.250289	2284.051658	0
63	131	F	60	0.916706	0.566121	0.512857	1.467165	6.372832	3.251168	3.539229	...	-0.046235	0.041946	-0.065313	-0.016682	0.061026	-0.005883	6.972152	2792.655884	1518.529172	0

5 rows × 135 columns

# information of data and data types
for i  in data.columns:
    print(f"'{i}'  data type: {data[i].dtypes}")

'ID'  data type: int64
'Sex'  data type: object
'Age'  data type: int64
'J1_a'  data type: float64
'J3_a'  data type: float64
'J5_a'  data type: float64
'J55_a'  data type: float64
'S1_a'  data type: float64
'S3_a'  data type: float64
'S5_a'  data type: float64
'S11_a'  data type: float64
'S55_a'  data type: float64
'DPF_a'  data type: float64
'PFR_a'  data type: float64
'PPE_a'  data type: float64
'PVI_a'  data type: float64
'HNR_a'  data type: float64
'GNEa_{\mu}'  data type: float64
'GNEa_{\sigma}'  data type: float64
'Ha(1)_{mu}'  data type: float64
'Ha(2)_{mu}'  data type: float64
'Ha(3)_{mu}'  data type: float64
'Ha(4)_{mu}'  data type: float64
'Ha(5)_{mu}'  data type: float64
'Ha(6)_{mu}'  data type: float64
'Ha(7)_{mu}'  data type: float64
'Ha(8)_{mu}'  data type: float64
'Ha(1)_{sd}'  data type: float64
'Ha(2)_{sd}'  data type: float64
'Ha(3)_{sd}'  data type: float64
'Ha(4)_{sd}'  data type: float64
'Ha(5)_{sd}'  data type: float64
'Ha(6)_{sd}'  data type: float64
'Ha(7)_{sd}'  data type: float64
'Ha(8)_{sd}'  data type: float64
'Ha(1)_{rel}'  data type: float64
'Ha(2)_{rel}'  data type: float64
'Ha(3)_{rel}'  data type: float64
'Ha(4)_{rel}'  data type: float64
'Ha(5)_{rel}'  data type: float64
'Ha(6)_{rel}'  data type: float64
'Ha(7)_{rel}'  data type: float64
'Ha(8)_{rel}'  data type: float64
'CCa(1)'  data type: float64
'CCa(2)'  data type: float64
'CCa(3)'  data type: float64
'CCa(4)'  data type: float64
'CCa(5)'  data type: float64
'CCa(6)'  data type: float64
'CCa(7)'  data type: float64
'CCa(8)'  data type: float64
'CCa(9)'  data type: float64
'CCa(10)'  data type: float64
'CCa(11)'  data type: float64
'CCa(12)'  data type: float64
'dCCa(1)'  data type: float64
'dCCa(2)'  data type: float64
'dCCa(3)'  data type: float64
'dCCa(4)'  data type: float64
'dCCa(5)'  data type: float64
'dCCa(6)'  data type: float64
'dCCa(7)'  data type: float64
'dCCa(8)'  data type: float64
'dCCa(9)'  data type: float64
'dCCa(10)'  data type: float64
'dCCa(11)'  data type: float64
'dCCa(12)'  data type: float64
'J1_i'  data type: float64
'J3_i'  data type: float64
'J5_i'  data type: float64
'J55_i'  data type: float64
'S1_i'  data type: float64
'S3_i'  data type: float64
'S5_i'  data type: float64
'S11_i'  data type: float64
'S55_i'  data type: float64
'DPF_i'  data type: float64
'PFR_i'  data type: float64
'PPE_i'  data type: float64
'PVI_i'  data type: float64
'HNR_i'  data type: float64
'GNEi_{\mu}'  data type: float64
'GNEi_{\sigma}'  data type: float64
'Hi(1)_{mu}'  data type: float64
'Hi(2)_{mu}'  data type: float64
'Hi(3)_{mu}'  data type: float64
'Hi(4)_{mu}'  data type: float64
'Hi(5)_{mu}'  data type: float64
'Hi(6)_{mu}'  data type: float64
'Hi(7)_{mu}'  data type: float64
'Hi(8)_{mu}'  data type: float64
'Hi(1)_{sd}'  data type: float64
'Hi(2)_{sd}'  data type: float64
'Hi(3)_{sd}'  data type: float64
'Hi(4)_{sd}'  data type: float64
'Hi(5)_{sd}'  data type: float64
'Hi(6)_{sd}'  data type: float64
'Hi(7)_{sd}'  data type: float64
'Hi(8)_{sd}'  data type: float64
'Hi(1)_{rel}'  data type: float64
'Hi(2)_{rel}'  data type: float64
'Hi(3)_{rel}'  data type: float64
'Hi(4)_{rel}'  data type: float64
'Hi(5)_{rel}'  data type: float64
'Hi(6)_{rel}'  data type: float64
'Hi(7)_{rel}'  data type: float64
'Hi(8)_{rel}'  data type: float64
'CCi(1)'  data type: float64
'CCi(2)'  data type: float64
'CCi(3)'  data type: float64
'CCi(4)'  data type: float64
'CCi(5)'  data type: float64
'CCi(6)'  data type: float64
'CCi(7)'  data type: float64
'CCi(8)'  data type: float64
'CCi(9)'  data type: float64
'CCi(10)'  data type: float64
'CCi(11)'  data type: float64
'CCi(12)'  data type: float64
'dCCi(1)'  data type: float64
'dCCi(2)'  data type: float64
'dCCi(3)'  data type: float64
'dCCi(4)'  data type: float64
'dCCi(5)'  data type: float64
'dCCi(6)'  data type: float64
'dCCi(7)'  data type: float64
'dCCi(8)'  data type: float64
'dCCi(9)'  data type: float64
'dCCi(10)'  data type: float64
'dCCi(11)'  data type: float64
'dCCi(12)'  data type: float64
'd_1'  data type: float64
'F2_i'  data type: float64
'F2_{conv}'  data type: float64
'Diagnosis (ALS)'  data type: int64

#find out the data shape (how many columns and how many rows are there)
data.shape

(64, 135)

#describe of data  Like count, Mean, Std, Min, quantile(25%,50%,75%), Max
data.describe().T

	count	mean	std	min	25%	50%	75%	max
ID	64.0	67.875000	37.358198	2.000000	31.750000	66.500000	98.250000	131.000000
Age	64.0	56.390625	10.203668	34.000000	50.750000	58.000000	63.250000	80.000000
J1_a	64.0	0.658951	0.724002	0.098881	0.325932	0.458935	0.772783	5.391649
J3_a	64.0	0.379242	0.435636	0.065791	0.172422	0.253976	0.465699	3.217293
J5_a	64.0	0.395886	0.431926	0.092655	0.198274	0.293405	0.476541	3.321567
...	...	...	...	...	...	...	...	...
dCCi(12)	64.0	0.001269	0.020800	-0.083977	-0.006534	0.000661	0.009515	0.077897
d_1	64.0	9.164473	2.681449	2.276702	7.604734	9.646564	10.757522	15.420777
F2_i	64.0	2495.116475	617.755856	444.730268	2051.627447	2471.097222	2938.236560	3599.554394
F2_{conv}	64.0	1209.976405	553.694046	48.246203	800.181156	1206.596083	1551.677678	2441.219054
Diagnosis (ALS)	64.0	0.484375	0.503706	0.000000	0.000000	0.000000	1.000000	1.000000

134 rows × 8 columns

# cross check informtion on Sex Like Male and Females
pd.crosstab(data.Sex,data['Diagnosis (ALS)'])

Diagnosis (ALS)	0	1
Sex
F	20	14
M	13	17

# cross check informtion on Sex And Ages
pd.crosstab(data.Age,data.Sex)

Sex	F	M
Age
34	0	1
35	0	1
37	1	0
38	0	2
39	2	0
40	1	1
41	0	1
43	0	1
45	1	0
49	0	1
50	3	0
51	0	1
52	1	1
53	2	0
55	3	0
57	2	3
58	1	3
59	1	0
60	3	3
61	0	2
62	2	0
63	3	1
64	3	0
65	1	0
66	0	1
67	2	3
68	1	2
69	0	1
70	1	0
80	0	1

# We are seeing how many age people are there using on Countplot
plt.figure(figsize=(12,8))


age=sns.countplot(x='Age', data=data,)

for i in age.containers:
    age.bar_label(i)

plt.figure(figsize=(12,8))


Gender=sns.countplot(x='Sex', data=data)

for i in Gender.containers:
    Gender.bar_label(i)

# Convert 'Male' to 0, 'Female' to 1

data["Sex"] =data['Sex'].replace({'M': 0, 'F': 1})

data.head()

	ID	Sex	Age	J1_a	J3_a	J5_a	J55_a	S1_a	S3_a	S5_a	...	dCCi(7)	dCCi(8)	dCCi(9)	dCCi(10)	dCCi(11)	dCCi(12)	d_1	F2_i	F2_{conv}	Diagnosis (ALS)
0	8	0	58	0.321817	0.141230	0.199128	0.923634	6.044559	3.196477	3.770575	...	-0.024467	-0.005300	0.051874	-0.037710	-0.026549	-0.021149	4.825476	2526.285657	833.498083	1
1	20	1	57	0.344026	0.177032	0.206458	0.827714	1.967728	0.856639	1.179851	...	0.002485	-0.004535	-0.000225	-0.006977	-0.012510	0.014773	5.729322	1985.712014	561.802625	1
2	21	1	58	0.264740	0.148228	0.177078	0.532566	1.850893	0.942743	1.071950	...	-0.013927	0.007908	0.007960	-0.009022	-0.012488	-0.015588	8.258488	2364.695972	796.723440	1
3	22	1	70	0.455793	0.174870	0.243660	0.962641	2.883768	1.284926	1.915058	...	-0.019285	-0.021768	0.020495	0.035976	-0.034648	0.008021	5.447137	1860.172768	359.409974	1
4	24	0	66	0.269335	0.143961	0.167465	0.547745	2.327924	1.164109	1.420891	...	-0.005743	0.004726	-0.015247	0.003900	-0.007686	-0.003784	8.562517	2051.627447	817.111847	1

5 rows × 135 columns

data['Age'].value_counts()

60    6
67    5
57    5
58    4
63    4
68    3
50    3
55    3
64    3
38    2
53    2
39    2
52    2
40    2
61    2
62    2
37    1
65    1
49    1
59    1
80    1
45    1
34    1
35    1
70    1
66    1
41    1
51    1
69    1
43    1
Name: Age, dtype: int64

data['Age'].mean()

56.390625

data['Age'].median()

58.0

columns= data.select_dtypes(include="number").columns

#see the histogramplots column wise
for column in data:
    plt.figure(figsize=(10, 6))
    k = data[column].kurt()
    s = data[column].skew()
    sns.histplot(data[column], kde=True, label= f'kurtosis: {k} & skewness: {s}')
    plt.title(f'Histogram of {column}')
    plt.legend()
    plt.show()

for col in columns:
    plt.boxplot(data[col])
    plt.title(f'Boxplot of {col}')
    plt.xlabel(col)
    plt.ylabel("frequency")
    plt.tight_layout()
    plt.show()

# create the dictionaries for statistics
means = {}
stds = {}
maxs = {}
mins = {}
vars = {}


# Calculate statistics for each column
for i in data.columns:
    means[i] = data[i].mean()
    stds[i] = data[i].std()
    maxs[i] = data[i].max()
    mins[i] = data[i].min()
    vars[i] = data[i].var()  
    
    print(f"Column: {i}")
    print(f"Mean: {means[i]}")
    print(f"Standard Deviation: {stds[i]}")
    print(f"Max: {maxs[i]}")
    print(f"Min: {mins[i]}")
    print(f"Var: {vars[i]}")
    print('-----'*10)

Column: ID
Mean: 67.875
Standard Deviation: 37.35819750248827
Max: 131
Min: 2
Var: 1395.6349206349207
--------------------------------------------------
Column: Sex
Mean: 0.53125
Standard Deviation: 0.5029673851018478
Max: 1
Min: 0
Var: 0.25297619047619047
--------------------------------------------------
Column: Age
Mean: 56.390625
Standard Deviation: 10.203667544035643
Max: 80
Min: 34
Var: 104.11483134920636
--------------------------------------------------
Column: J1_a
Mean: 0.6589510923264591
Standard Deviation: 0.7240019797240058
Max: 5.39164911025152
Min: 0.0988808740961693
Var: 0.5241788666442797
--------------------------------------------------
Column: J3_a
Mean: 0.37924166747964577
Standard Deviation: 0.4356357937217889
Max: 3.21729269682184
Min: 0.0657908570911333
Var: 0.18977854477161302
--------------------------------------------------
Column: J5_a
Mean: 0.3958855278231064
Standard Deviation: 0.43192613709803857
Max: 3.32156682922536
Min: 0.092654708047529
Var: 0.1865601879084336
--------------------------------------------------
Column: J55_a
Mean: 0.9454963659113814
Standard Deviation: 0.7915578601815897
Max: 5.99133551977937
Min: 0.285496902227754
Var: 0.6265638460152572
--------------------------------------------------
Column: S1_a
Mean: 5.072070662672618
Standard Deviation: 4.087220998241304
Max: 29.4415893863429
Min: 0.883452930509886
Var: 16.70537548846464
--------------------------------------------------
Column: S3_a
Mean: 2.6179237242153452
Standard Deviation: 2.2893472434768816
Max: 16.7919435942875
Min: 0.449099045020366
Var: 5.241110801215196
--------------------------------------------------
Column: S5_a
Mean: 3.078958803740799
Standard Deviation: 2.568470618665079
Max: 18.3687775701395
Min: 0.49711113337741
Var: 6.5970413189457755
--------------------------------------------------
Column: S11_a
Mean: 4.393513494458013
Standard Deviation: 3.3279682893894362
Max: 20.7502022793951
Min: 0.804178643837459
Var: 11.07537293518165
--------------------------------------------------
Column: S55_a
Mean: 8.218759976060985
Standard Deviation: 5.429425241182339
Max: 27.8886408329676
Min: 2.12300082346364
Var: 29.4786584495879
--------------------------------------------------
Column: DPF_a
Mean: 66.60136730583282
Standard Deviation: 12.010272254272317
Max: 87.6790830945559
Min: 26.6545784224841
Var: 144.24663962174344
--------------------------------------------------
Column: PFR_a
Mean: 0.22950795241187172
Standard Deviation: 0.2994210517717801
Max: 2.05988326586503
Min: 0.0614092277753447
Var: 0.08965296624411902
--------------------------------------------------
Column: PPE_a
Mean: 1.04610081933792
Standard Deviation: 0.663761326486491
Max: 3.39377357252711
Min: 0.0397935998137785
Var: 0.44057909853910604
--------------------------------------------------
Column: PVI_a
Mean: 0.48633739351358796
Standard Deviation: 0.44774998289264556
Max: 3.152777509126
Min: -0.0
Var: 0.20048004718036438
--------------------------------------------------
Column: HNR_a
Mean: 13.11754261828827
Standard Deviation: 5.158557315941071
Max: 23.8771239088319
Min: 1.22474104719174
Var: 26.61071358184914
--------------------------------------------------
Column: GNEa_{\mu}
Mean: 0.9056801674574759
Standard Deviation: 0.034267591654726315
Max: 0.968958874167239
Min: 0.847778548421272
Var: 0.0011742678378150685
--------------------------------------------------
Column: GNEa_{\sigma}
Mean: 0.030288750669149076
Standard Deviation: 0.009127858804112524
Max: 0.0601045491237549
Min: 0.0084854242670405
Var: 8.331780634781452e-05
--------------------------------------------------
Column: Ha(1)_{mu}
Mean: -10.918389796275394
Standard Deviation: 8.750774550325888
Max: -1.02523729551332
Min: -42.7302212787659
Var: 76.57605523063124
--------------------------------------------------
Column: Ha(2)_{mu}
Mean: -11.898157865612609
Standard Deviation: 5.971155619367993
Max: -4.27002356405569
Min: -30.0986635852088
Var: 35.654699430709954
--------------------------------------------------
Column: Ha(3)_{mu}
Mean: -13.027749772097136
Standard Deviation: 6.634671926854527
Max: -1.88584415369332
Min: -40.0069615836161
Var: 44.01887157699157
--------------------------------------------------
Column: Ha(4)_{mu}
Mean: -15.332621053473595
Standard Deviation: 8.455905570067294
Max: -1.81235734598376
Min: -46.4308820673431
Var: 71.5023390098951
--------------------------------------------------
Column: Ha(5)_{mu}
Mean: -20.211281972134486
Standard Deviation: 11.153857303180878
Max: -2.17357664958254
Min: -51.4969461495609
Var: 124.4085327397214
--------------------------------------------------
Column: Ha(6)_{mu}
Mean: -22.606453857292248
Standard Deviation: 12.314214435100542
Max: -3.34129255097787
Min: -64.8940530659436
Var: 151.63987715363857
--------------------------------------------------
Column: Ha(7)_{mu}
Mean: -27.597269376750464
Standard Deviation: 12.712163748682812
Max: -6.22010305162356
Min: -59.6414868673019
Var: 161.59910717332545
--------------------------------------------------
Column: Ha(8)_{mu}
Mean: -32.584852802140205
Standard Deviation: 13.539554443493675
Max: -5.26183857168348
Min: -64.0606439484734
Var: 183.31953452832929
--------------------------------------------------
Column: Ha(1)_{sd}
Mean: 2.4924325404369427
Standard Deviation: 2.6301112921292327
Max: 15.4170922344652
Min: 0.456430854993122
Var: 6.917485408985701
--------------------------------------------------
Column: Ha(2)_{sd}
Mean: 2.86299501480734
Standard Deviation: 2.0494753274997275
Max: 8.24580631710921
Min: 0.628063815299804
Var: 4.200349118030116
--------------------------------------------------
Column: Ha(3)_{sd}
Mean: 3.4641430013719354
Standard Deviation: 2.7575819214897352
Max: 15.1686200587276
Min: 0.673782105840455
Var: 7.60425805372702
--------------------------------------------------
Column: Ha(4)_{sd}
Mean: 3.789089322853057
Standard Deviation: 2.4103575917869717
Max: 10.0352087235906
Min: 1.08458181097138
Var: 5.80982372028509
--------------------------------------------------
Column: Ha(5)_{sd}
Mean: 4.597379057006017
Standard Deviation: 2.7349233400650723
Max: 15.5240670209037
Min: 0.901755105979975
Var: 7.47980567603269
--------------------------------------------------
Column: Ha(6)_{sd}
Mean: 4.798836466900102
Standard Deviation: 2.957814249056782
Max: 19.0559594468549
Min: 0.754808433773203
Var: 8.748665131923335
--------------------------------------------------
Column: Ha(7)_{sd}
Mean: 4.992635204732386
Standard Deviation: 3.3015583269477715
Max: 20.8839801187164
Min: 1.25072855119363
Var: 10.900287386238169
--------------------------------------------------
Column: Ha(8)_{sd}
Mean: 5.238973251316987
Standard Deviation: 2.655395398410618
Max: 15.4436524486257
Min: 1.23350033859106
Var: 7.051124721900284
--------------------------------------------------
Column: Ha(1)_{rel}
Mean: 0.13265750108780114
Standard Deviation: 0.11554086161146618
Max: 0.674914959640725
Min: 0.0195647194135768
Var: 0.01334969070191998
--------------------------------------------------
Column: Ha(2)_{rel}
Mean: 0.08498439400871834
Standard Deviation: 0.03912391137402449
Max: 0.184987225617616
Min: 0.0286255905741229
Var: 0.0015306804412025226
--------------------------------------------------
Column: Ha(3)_{rel}
Mean: 0.08004200645314209
Standard Deviation: 0.05400708580313946
Max: 0.355466617459184
Min: 0.018123959371777
Var: 0.0029167653169476677
--------------------------------------------------
Column: Ha(4)_{rel}
Mean: 0.07359893405284057
Standard Deviation: 0.056228038927270824
Max: 0.345191923551153
Min: 0.0186870767484907
Var: 0.003161592361606683
--------------------------------------------------
Column: Ha(5)_{rel}
Mean: 0.05987508437652133
Standard Deviation: 0.04879107738666688
Max: 0.283583798329672
Min: 0.0171265901741955
Var: 0.0023805692325517165
--------------------------------------------------
Column: Ha(6)_{rel}
Mean: 0.05233025283525436
Standard Deviation: 0.04254215708378476
Max: 0.229415505504976
Min: 0.0141680571417519
Var: 0.0018098351293414179
--------------------------------------------------
Column: Ha(7)_{rel}
Mean: 0.0381402459175993
Standard Deviation: 0.019359151877006125
Max: 0.100367151246796
Min: 0.0148963624373277
Var: 0.00037477676139698973
--------------------------------------------------
Column: Ha(8)_{rel}
Mean: 0.03232225660923279
Standard Deviation: 0.01894169659614182
Max: 0.137463732412042
Min: 0.0137153309359801
Var: 0.0003587878699402906
--------------------------------------------------
Column: CCa(1)
Mean: 1.1121905609616427
Standard Deviation: 4.949993221376777
Max: 12.2917747138599
Min: -12.3587760924942
Var: 24.502432891676047
--------------------------------------------------
Column: CCa(2)
Mean: -9.724182973941003
Standard Deviation: 5.553031801942787
Max: 2.57812014607364
Min: -25.4073215651635
Var: 30.83616219338795
--------------------------------------------------
Column: CCa(3)
Mean: -8.898135659514084
Standard Deviation: 6.495814907269197
Max: 4.0321351235004
Min: -25.9796004012163
Var: 42.19561130950073
--------------------------------------------------
Column: CCa(4)
Mean: -2.717699172526518
Standard Deviation: 6.675439731397148
Max: 15.3537936386388
Min: -18.5663528157525
Var: 44.561495607515624
--------------------------------------------------
Column: CCa(5)
Mean: -9.280586668507024
Standard Deviation: 8.312616528402945
Max: 17.1238445939678
Min: -27.3351022716619
Var: 69.09959354827782
--------------------------------------------------
Column: CCa(6)
Mean: -1.068055026938378
Standard Deviation: 7.390385524488313
Max: 15.6355280858561
Min: -20.0699436111426
Var: 54.617798200566405
--------------------------------------------------
Column: CCa(7)
Mean: -3.2801734129252536
Standard Deviation: 6.298097866238561
Max: 17.0931348771196
Min: -18.2747015228283
Var: 39.666036732718716
--------------------------------------------------
Column: CCa(8)
Mean: -4.366733226819115
Standard Deviation: 10.051911699315342
Max: 20.1330539137104
Min: -23.105108657258
Var: 101.04092881083265
--------------------------------------------------
Column: CCa(9)
Mean: -9.386150474969893
Standard Deviation: 6.792950491165196
Max: 16.6454609819757
Min: -25.843250178978
Var: 46.14417637542147
--------------------------------------------------
Column: CCa(10)
Mean: -5.815643068210756
Standard Deviation: 6.6461210337497
Max: 16.8582063590473
Min: -18.8940787223355
Var: 44.17092479525018
--------------------------------------------------
Column: CCa(11)
Mean: 0.08758968092139799
Standard Deviation: 7.287971843554155
Max: 28.7979477874746
Min: -13.47154437876
Var: 53.11453359243816
--------------------------------------------------
Column: CCa(12)
Mean: -1.9915715915891292
Standard Deviation: 6.61907039060938
Max: 19.3498288831846
Min: -14.2630022613835
Var: 43.81209283584182
--------------------------------------------------
Column: dCCa(1)
Mean: 0.001382954759202585
Standard Deviation: 0.013686810687317603
Max: 0.0562197465208467
Min: -0.0388791350237649
Var: 0.00018732878679047135
--------------------------------------------------
Column: dCCa(2)
Mean: 0.0076527818262686325
Standard Deviation: 0.01839895758985901
Max: 0.0687695697918272
Min: -0.0159711482044381
Var: 0.0003385216403934305
--------------------------------------------------
Column: dCCa(3)
Mean: 0.0008137051752666842
Standard Deviation: 0.01577443139218938
Max: 0.0274413869154998
Min: -0.0611631000227483
Var: 0.0002488326857468898
--------------------------------------------------
Column: dCCa(4)
Mean: -0.0016348086025046267
Standard Deviation: 0.018044798499730463
Max: 0.0389936640207402
Min: -0.0561977757601556
Var: 0.0003256147528958747
--------------------------------------------------
Column: dCCa(5)
Mean: -0.002202510461326893
Standard Deviation: 0.02767145053959421
Max: 0.0712920913999485
Min: -0.140435284232214
Var: 0.0007657091749652087
--------------------------------------------------
Column: dCCa(6)
Mean: 0.0018610229445092667
Standard Deviation: 0.02010253628312101
Max: 0.0536857974178037
Min: -0.0488007460084803
Var: 0.00040411196501419667
--------------------------------------------------
Column: dCCa(7)
Mean: 0.004432238927951802
Standard Deviation: 0.02129172122862291
Max: 0.0754125459017925
Min: -0.0428794013721995
Var: 0.00045333739287739137
--------------------------------------------------
Column: dCCa(8)
Mean: 0.0006500342763628977
Standard Deviation: 0.026354421108471793
Max: 0.148294367935822
Min: -0.0549806751736176
Var: 0.0006945555119626637
--------------------------------------------------
Column: dCCa(9)
Mean: 0.0011826148117677238
Standard Deviation: 0.025877709548438717
Max: 0.0792218341030567
Min: -0.092013581142057
Var: 0.0006696558514733564
--------------------------------------------------
Column: dCCa(10)
Mean: 0.0028273042884651477
Standard Deviation: 0.020172363491495836
Max: 0.0897900270349725
Min: -0.0490835843082015
Var: 0.00040692424883303406
--------------------------------------------------
Column: dCCa(11)
Mean: 0.0008351569383335656
Standard Deviation: 0.0228130794368141
Max: 0.0696827319084076
Min: -0.0603578451111036
Var: 0.0005204365933903903
--------------------------------------------------
Column: dCCa(12)
Mean: -0.00042300580950935577
Standard Deviation: 0.027344595434515003
Max: 0.0846943139702484
Min: -0.105210256261341
Var: 0.0007477268994772987
--------------------------------------------------
Column: J1_i
Mean: 0.5604972902107235
Standard Deviation: 0.4677865299222095
Max: 2.54574974297934
Min: 0.124382709607886
Var: 0.2188242375766622
--------------------------------------------------
Column: J3_i
Mean: 0.32211696508283766
Standard Deviation: 0.2936920901539547
Max: 1.53583002017611
Min: 0.063500096867429
Var: 0.08625504381899865
--------------------------------------------------
Column: J5_i
Mean: 0.3259531001300733
Standard Deviation: 0.23423841148021868
Max: 1.26977165757309
Min: 0.107823935317913
Var: 0.05486763341277624
--------------------------------------------------
Column: J55_i
Mean: 0.7887940005128145
Standard Deviation: 0.5323663841071605
Max: 3.00895758879809
Min: 0.270428744229768
Var: 0.28341396692733273
--------------------------------------------------
Column: S1_i
Mean: 3.428250895630246
Standard Deviation: 2.0717580091471284
Max: 9.8757130009375
Min: 0.85109505271316
Var: 4.2921812484652735
--------------------------------------------------
Column: S3_i
Mean: 1.7487684640968078
Standard Deviation: 1.1536607414763118
Max: 5.03562066564665
Min: 0.437936732853038
Var: 1.3309331064236736
--------------------------------------------------
Column: S5_i
Mean: 2.04150931066711
Standard Deviation: 1.269170715197415
Max: 6.15231885044359
Min: 0.462263216331383
Var: 1.6107943043147177
--------------------------------------------------
Column: S11_i
Mean: 2.894741527244938
Standard Deviation: 1.6783774447194408
Max: 8.43105387693632
Min: 0.575181928238406
Var: 2.81695084694296
--------------------------------------------------
Column: S55_i
Mean: 6.101996131551487
Standard Deviation: 3.2018254261739543
Max: 16.1666335981014
Min: 1.90147943006464
Var: 10.251686059694025
--------------------------------------------------
Column: DPF_i
Mean: 65.13435832614583
Standard Deviation: 12.896304583578306
Max: 86.4864864864865
Min: 30.8835672997523
Var: 166.31467191242282
--------------------------------------------------
Column: PFR_i
Mean: 0.20736610708010947
Standard Deviation: 0.19797235028045063
Max: 1.1230817546142
Min: 0.0613346721225932
Var: 0.03919305147556544
--------------------------------------------------
Column: PPE_i
Mean: 1.2505767241591745
Standard Deviation: 0.6377994671119896
Max: 3.22205694616239
Min: 0.142582177534859
Var: 0.406788160248338
--------------------------------------------------
Column: PVI_i
Mean: 0.516292916208627
Standard Deviation: 0.515401293055496
Max: 3.01306679375731
Min: -0.0
Var: 0.26563849288327723
--------------------------------------------------
Column: HNR_i
Mean: 19.609512607806323
Standard Deviation: 4.9312672149769
Max: 29.0340710410429
Min: 6.83156778933749
Var: 24.317396345506033
--------------------------------------------------
Column: GNEi_{\mu}
Mean: 0.8980164404225391
Standard Deviation: 0.03276622052047653
Max: 0.97685205701766
Min: 0.843636170166613
Var: 0.001073625207196497
--------------------------------------------------
Column: GNEi_{\sigma}
Mean: 0.02891886026416772
Standard Deviation: 0.006668838808495691
Max: 0.0413542210284205
Min: 0.0079383460373362
Var: 4.447341105369823e-05
--------------------------------------------------
Column: Hi(1)_{mu}
Mean: -10.171170071618485
Standard Deviation: 10.59135936953954
Max: -1.6123241034975
Min: -48.9008369794071
Var: 112.17689329473299
--------------------------------------------------
Column: Hi(2)_{mu}
Mean: -10.917329242937706
Standard Deviation: 8.453458552292123
Max: -1.23166184165386
Min: -35.7961652939104
Var: 71.46096149532082
--------------------------------------------------
Column: Hi(3)_{mu}
Mean: -25.73291393462567
Standard Deviation: 15.347472820276503
Max: -1.84670381969273
Min: -71.0776600359972
Var: 235.54492196912597
--------------------------------------------------
Column: Hi(4)_{mu}
Mean: -36.913112508500554
Standard Deviation: 17.009739134555
Max: -3.76100753736233
Min: -68.4671961331809
Var: 289.331225425612
--------------------------------------------------
Column: Hi(5)_{mu}
Mean: -46.558202854723596
Standard Deviation: 14.639661938976184
Max: -11.0536388959591
Min: -74.1226599560466
Var: 214.31970168750792
--------------------------------------------------
Column: Hi(6)_{mu}
Mean: -49.65970962021661
Standard Deviation: 13.84487289300312
Max: -5.29000422636931
Min: -73.8368160123499
Var: 191.6805054234126
--------------------------------------------------
Column: Hi(7)_{mu}
Mean: -52.49458041059174
Standard Deviation: 11.834121359166746
Max: -11.8895881558621
Min: -73.2330156125233
Var: 140.04642834348658
--------------------------------------------------
Column: Hi(8)_{mu}
Mean: -53.026724461458734
Standard Deviation: 12.602194004332926
Max: -14.7268388273915
Min: -76.7677744664689
Var: 158.81529372284473
--------------------------------------------------
Column: Hi(1)_{sd}
Mean: 2.4278352275918866
Standard Deviation: 2.30551593881782
Max: 11.9729344550696
Min: 0.388797010213913
Var: 5.3154037441430155
--------------------------------------------------
Column: Hi(2)_{sd}
Mean: 2.958613822314677
Standard Deviation: 2.5703429688325587
Max: 13.1298096167449
Min: 0.573949129866831
Var: 6.606662977426973
--------------------------------------------------
Column: Hi(3)_{sd}
Mean: 3.7475815752498334
Standard Deviation: 2.4976635388917803
Max: 14.2658139827342
Min: 0.899958987140613
Var: 6.238323153509412
--------------------------------------------------
Column: Hi(4)_{sd}
Mean: 4.029022933184554
Standard Deviation: 2.2850899902864814
Max: 9.934408710848
Min: 0.647605098022009
Var: 5.221636263707472
--------------------------------------------------
Column: Hi(5)_{sd}
Mean: 4.648561980089846
Standard Deviation: 2.267330598392807
Max: 13.0645308884366
Min: 0.809010640808776
Var: 5.140788042408285
--------------------------------------------------
Column: Hi(6)_{sd}
Mean: 4.8027824497096905
Standard Deviation: 2.0776786409211763
Max: 9.52645385682456
Min: 1.12269830426427
Var: 4.316748534940066
--------------------------------------------------
Column: Hi(7)_{sd}
Mean: 5.2634855107732275
Standard Deviation: 2.1464844058163783
Max: 11.968620375951
Min: 1.34866330979525
Var: 4.60739530441289
--------------------------------------------------
Column: Hi(8)_{sd}
Mean: 5.224679280549857
Standard Deviation: 2.168108949508015
Max: 11.1326713190622
Min: 1.8687080560454
Var: 4.700696416936749
--------------------------------------------------
Column: Hi(1)_{rel}
Mean: 0.15205975398202073
Standard Deviation: 0.11088728740013579
Max: 0.416235733630038
Min: 0.0181452927589843
Var: 0.012295990506960312
--------------------------------------------------
Column: Hi(2)_{rel}
Mean: 0.12273679460895721
Standard Deviation: 0.09690976024069513
Max: 0.47120073818453
Min: 0.0233400697961505
Var: 0.009391501629909016
--------------------------------------------------
Column: Hi(3)_{rel}
Mean: 0.05892235693997809
Standard Deviation: 0.06473114571676598
Max: 0.36407818153438
Min: 0.0130203933606143
Var: 0.00419012122580519
--------------------------------------------------
Column: Hi(4)_{rel}
Mean: 0.035004429931756904
Standard Deviation: 0.03149241922593493
Max: 0.174463150055721
Min: 0.0129178399821179
Var: 0.000991772468702036
--------------------------------------------------
Column: Hi(5)_{rel}
Mean: 0.02214737872770327
Standard Deviation: 0.009766908998302017
Max: 0.0718874855529159
Min: 0.0124761518420045
Var: 9.53925113811129e-05
--------------------------------------------------
Column: Hi(6)_{rel}
Mean: 0.02133538836006788
Standard Deviation: 0.014629121713532858
Max: 0.121484894078625
Min: 0.0124705884941843
Var: 0.00021401120210935855
--------------------------------------------------
Column: Hi(7)_{rel}
Mean: 0.01859028886198358
Standard Deviation: 0.006997438790482036
Max: 0.0603290347732484
Min: 0.0123648174689675
Var: 4.896414962654269e-05
--------------------------------------------------
Column: Hi(8)_{rel}
Mean: 0.018504326504890412
Standard Deviation: 0.006555775759198389
Max: 0.0540434345491743
Min: 0.012195376364715
Var: 4.2978195804893215e-05
--------------------------------------------------
Column: CCi(1)
Mean: -0.03681474942688663
Standard Deviation: 4.756599529494893
Max: 10.7989006470902
Min: -12.2609040135642
Var: 22.625239083991033
--------------------------------------------------
Column: CCi(2)
Mean: 12.668709484671437
Standard Deviation: 8.580686864222983
Max: 26.2731092461923
Min: -14.8975515227695
Var: 73.62818706184883
--------------------------------------------------
Column: CCi(3)
Mean: -5.886748430956232
Standard Deviation: 8.27866857812091
Max: 8.3067796957193
Min: -22.9769853579478
Var: 68.53635342636652
--------------------------------------------------
Column: CCi(4)
Mean: -16.331894916287037
Standard Deviation: 7.932550333030245
Max: 13.3814060548556
Min: -31.3288578588504
Var: 62.92535478605825
--------------------------------------------------
Column: CCi(5)
Mean: -10.861020784556386
Standard Deviation: 7.211648416847074
Max: 6.28960228754939
Min: -23.6769493827959
Var: 52.0078728882129
--------------------------------------------------
Column: CCi(6)
Mean: -7.65742962711239
Standard Deviation: 8.095678597262246
Max: 8.93262955204458
Min: -21.7832948089145
Var: 65.54001195017001
--------------------------------------------------
Column: CCi(7)
Mean: -8.921400737277457
Standard Deviation: 6.694698184456786
Max: 5.74506869738599
Min: -30.7254921869464
Var: 44.81898378096898
--------------------------------------------------
Column: CCi(8)
Mean: -3.588996697428346
Standard Deviation: 7.657291967775357
Max: 13.1291325623703
Min: -26.1902001586054
Var: 58.634120279757
--------------------------------------------------
Column: CCi(9)
Mean: -4.736833518756158
Standard Deviation: 7.484008186103914
Max: 17.8393830825827
Min: -20.145785639623
Var: 56.0103785296704
--------------------------------------------------
Column: CCi(10)
Mean: -5.528360754375791
Standard Deviation: 6.979685584065822
Max: 17.6170866115972
Min: -17.4591314170075
Var: 48.71601085241626
--------------------------------------------------
Column: CCi(11)
Mean: -0.48551152452437424
Standard Deviation: 8.371665111227859
Max: 20.1472796194926
Min: -15.7302768628524
Var: 70.08477673454975
--------------------------------------------------
Column: CCi(12)
Mean: -2.818272171557788
Standard Deviation: 7.8423688402829566
Max: 16.9284282530435
Min: -21.5918240288464
Var: 61.50274902704104
--------------------------------------------------
Column: dCCi(1)
Mean: 0.0036140233188376282
Standard Deviation: 0.012212330844588067
Max: 0.0568569111568067
Min: -0.0242549836971201
Var: 0.0001491410246576771
--------------------------------------------------
Column: dCCi(2)
Mean: -0.006828938085752231
Standard Deviation: 0.018187520588793707
Max: 0.0366060445292327
Min: -0.072591244327061
Var: 0.000330785905167795
--------------------------------------------------
Column: dCCi(3)
Mean: 0.004692481423131148
Standard Deviation: 0.017865364169368656
Max: 0.0687304279644546
Min: -0.0606761961658655
Var: 0.0003191712369041615
--------------------------------------------------
Column: dCCi(4)
Mean: -0.0056059722776925875
Standard Deviation: 0.019439075396967855
Max: 0.0514523078774652
Min: -0.0576347404331426
Var: 0.00037787765228900096
--------------------------------------------------
Column: dCCi(5)
Mean: 0.005055228539628075
Standard Deviation: 0.01819293361150992
Max: 0.0588495419869384
Min: -0.0660041917603545
Var: 0.0003309828333928074
--------------------------------------------------
Column: dCCi(6)
Mean: 0.0010846481326639028
Standard Deviation: 0.021640629742471315
Max: 0.0316242242612357
Min: -0.0728282781162606
Var: 0.00046831685565073406
--------------------------------------------------
Column: dCCi(7)
Mean: 0.0033927843187008255
Standard Deviation: 0.033267506223185535
Max: 0.220532793164975
Min: -0.0520018738969869
Var: 0.0011067269703096882
--------------------------------------------------
Column: dCCi(8)
Mean: 0.006224109568974981
Standard Deviation: 0.024670026432084005
Max: 0.0897656349030549
Min: -0.0692019476960927
Var: 0.0006086102041597234
--------------------------------------------------
Column: dCCi(9)
Mean: -0.0028885401807108857
Standard Deviation: 0.02831525762819393
Max: 0.0803366866296069
Min: -0.120838233459257
Var: 0.0008017538145509947
--------------------------------------------------
Column: dCCi(10)
Mean: -0.0021518296735254633
Standard Deviation: 0.019542196223942015
Max: 0.0573029885696565
Min: -0.0657350124404751
Var: 0.0003818974332550536
--------------------------------------------------
Column: dCCi(11)
Mean: -0.0019687410740259377
Standard Deviation: 0.01801151082040923
Max: 0.061026390026761
Min: -0.0429554525049006
Var: 0.0003244145220337188
--------------------------------------------------
Column: dCCi(12)
Mean: 0.0012688797448391893
Standard Deviation: 0.020799686929897636
Max: 0.0778972293782146
Min: -0.0839773697130102
Var: 0.0004326269763817546
--------------------------------------------------
Column: d_1
Mean: 9.164472684963322
Standard Deviation: 2.6814486649289866
Max: 15.4207766781969
Min: 2.27670168162266
Var: 7.190166942649444
--------------------------------------------------
Column: F2_i
Mean: 2495.116475499477
Standard Deviation: 617.7558556972639
Max: 3599.55439382624
Min: 444.730268116722
Var: 381622.2972482587
--------------------------------------------------
Column: F2_{conv}
Mean: 1209.9764046384807
Standard Deviation: 553.6940461956145
Max: 2441.21905442786
Min: 48.2462034430127
Var: 306577.09679247136
--------------------------------------------------
Column: Diagnosis (ALS)
Mean: 0.484375
Standard Deviation: 0.503706499953334
Max: 1
Min: 0
Var: 0.2537202380952381
--------------------------------------------------

percentiles_25 = {}
percentiles_50 = {}
percentiles_75 = {}

# Calculate statistics for each column
for i in data.columns:
    percentiles_25[i] = data[i].quantile(0.25)
    percentiles_50[i] = data[i].quantile(0.50)
    percentiles_75[i] = data[i].quantile(0.75)
    
    print(f"Column: {i}")
    print(f"25th percentile: {percentiles_25[i]}")
    print(f"50th percentile: {percentiles_50[i]}")
    print(f"75th percentile: {percentiles_75[i]}")
    print('------'*10)

Column: ID
25th percentile: 31.75
50th percentile: 66.5
75th percentile: 98.25
------------------------------------------------------------
Column: Sex
25th percentile: 0.0
50th percentile: 1.0
75th percentile: 1.0
------------------------------------------------------------
Column: Age
25th percentile: 50.75
50th percentile: 58.0
75th percentile: 63.25
------------------------------------------------------------
Column: J1_a
25th percentile: 0.325932212785935
50th percentile: 0.458934690690478
75th percentile: 0.7727828083997907
------------------------------------------------------------
Column: J3_a
25th percentile: 0.17242193306070952
50th percentile: 0.253975529337377
75th percentile: 0.465698958212338
------------------------------------------------------------
Column: J5_a
25th percentile: 0.19827438018164376
50th percentile: 0.29340494115155247
75th percentile: 0.47654100894346874
------------------------------------------------------------
Column: J55_a
25th percentile: 0.5383869803782271
50th percentile: 0.6981825444535985
75th percentile: 1.1890253510143725
------------------------------------------------------------
Column: S1_a
25th percentile: 2.64182962293309
50th percentile: 4.19848642131325
75th percentile: 6.259731337398078
------------------------------------------------------------
Column: S3_a
25th percentile: 1.36944605357487
50th percentile: 1.966045392147405
75th percentile: 3.2581840592869376
------------------------------------------------------------
Column: S5_a
25th percentile: 1.508064265817355
50th percentile: 2.65366648046679
75th percentile: 3.8015044583924174
------------------------------------------------------------
Column: S11_a
25th percentile: 2.1237609699215025
50th percentile: 3.63499441394943
75th percentile: 5.644808423124029
------------------------------------------------------------
Column: S55_a
25th percentile: 4.425068174740881
50th percentile: 6.46525534079523
75th percentile: 10.350968176784725
------------------------------------------------------------
Column: DPF_a
25th percentile: 60.15591401152575
50th percentile: 68.00716518103894
75th percentile: 76.62763978553456
------------------------------------------------------------
Column: PFR_a
25th percentile: 0.10552923004441075
50th percentile: 0.157785981277095
75th percentile: 0.253424120400572
------------------------------------------------------------
Column: PPE_a
25th percentile: 0.454098617738357
50th percentile: 0.977585138664433
75th percentile: 1.401583064808
------------------------------------------------------------
Column: PVI_a
25th percentile: 0.24778532955392202
50th percentile: 0.41838044928998996
75th percentile: 0.5509138050214244
------------------------------------------------------------
Column: HNR_a
25th percentile: 9.35662836648995
50th percentile: 12.880039281547301
75th percentile: 16.415745943934525
------------------------------------------------------------
Column: GNEa_{\mu}
25th percentile: 0.8753948310949548
50th percentile: 0.9052571130863696
75th percentile: 0.9316980340181847
------------------------------------------------------------
Column: GNEa_{\sigma}
25th percentile: 0.025686788305854826
50th percentile: 0.0310084870124
75th percentile: 0.03561833117145765
------------------------------------------------------------
Column: Ha(1)_{mu}
25th percentile: -14.231224955880075
50th percentile: -8.35935252117335
75th percentile: -4.273960221432798
------------------------------------------------------------
Column: Ha(2)_{mu}
25th percentile: -15.630161402294274
50th percentile: -10.3423065967396
75th percentile: -7.129745148695503
------------------------------------------------------------
Column: Ha(3)_{mu}
25th percentile: -16.5393741406053
50th percentile: -13.14197042458165
75th percentile: -8.372756404885452
------------------------------------------------------------
Column: Ha(4)_{mu}
25th percentile: -19.82266208050395
50th percentile: -14.034719648193551
75th percentile: -10.502018298339674
------------------------------------------------------------
Column: Ha(5)_{mu}
25th percentile: -28.520342636844077
50th percentile: -18.11069510681055
75th percentile: -12.258674852541375
------------------------------------------------------------
Column: Ha(6)_{mu}
25th percentile: -29.712325223814325
50th percentile: -21.711106352034548
75th percentile: -14.37830370481765
------------------------------------------------------------
Column: Ha(7)_{mu}
25th percentile: -33.44501282594473
50th percentile: -26.6856543647814
75th percentile: -17.330045525550872
------------------------------------------------------------
Column: Ha(8)_{mu}
25th percentile: -40.1478603985322
50th percentile: -30.0761793433272
75th percentile: -25.09165257978905
------------------------------------------------------------
Column: Ha(1)_{sd}
25th percentile: 0.9634092381783262
50th percentile: 1.435627714163515
75th percentile: 3.3586359078220527
------------------------------------------------------------
Column: Ha(2)_{sd}
25th percentile: 1.3541348550365977
50th percentile: 2.1610096881573204
75th percentile: 3.896733774094365
------------------------------------------------------------
Column: Ha(3)_{sd}
25th percentile: 1.7009920269378527
50th percentile: 2.6219531385459547
75th percentile: 3.9103795992219075
------------------------------------------------------------
Column: Ha(4)_{sd}
25th percentile: 1.9461853288964326
50th percentile: 2.988522177143105
75th percentile: 4.979344316616983
------------------------------------------------------------
Column: Ha(5)_{sd}
25th percentile: 2.42851248952423
50th percentile: 4.14654667456941
75th percentile: 6.362348692319035
------------------------------------------------------------
Column: Ha(6)_{sd}
25th percentile: 2.718376980715
50th percentile: 4.30367055540295
75th percentile: 6.2423375033158175
------------------------------------------------------------
Column: Ha(7)_{sd}
25th percentile: 2.6144649171345202
50th percentile: 3.8708026394941353
75th percentile: 6.50906829601836
------------------------------------------------------------
Column: Ha(8)_{sd}
25th percentile: 3.3050023766216725
50th percentile: 5.007648124611375
75th percentile: 7.090947028568673
------------------------------------------------------------
Column: Ha(1)_{rel}
25th percentile: 0.0585155949519144
50th percentile: 0.0997119788674579
75th percentile: 0.18036465293824
------------------------------------------------------------
Column: Ha(2)_{rel}
25th percentile: 0.05406167769680742
50th percentile: 0.07958688209849246
75th percentile: 0.11483918003935925
------------------------------------------------------------
Column: Ha(3)_{rel}
25th percentile: 0.049947252427341146
50th percentile: 0.0634977234499431
75th percentile: 0.09257261307822318
------------------------------------------------------------
Column: Ha(4)_{rel}
25th percentile: 0.042200883354431
50th percentile: 0.05697333885746845
75th percentile: 0.07975841345341134
------------------------------------------------------------
Column: Ha(5)_{rel}
25th percentile: 0.027691920944120225
50th percentile: 0.0444021045405419
75th percentile: 0.06703624147214945
------------------------------------------------------------
Column: Ha(6)_{rel}
25th percentile: 0.02739523339417915
50th percentile: 0.041349073494446095
75th percentile: 0.054670461332237574
------------------------------------------------------------
Column: Ha(7)_{rel}
25th percentile: 0.024661186091498602
50th percentile: 0.0330993785249601
75th percentile: 0.044613527454857194
------------------------------------------------------------
Column: Ha(8)_{rel}
25th percentile: 0.02193061916684545
50th percentile: 0.029766968681663
75th percentile: 0.033603019628248
------------------------------------------------------------
Column: CCa(1)
25th percentile: -2.39954996838877
50th percentile: 0.8120446345862056
75th percentile: 4.101239598353993
------------------------------------------------------------
Column: CCa(2)
25th percentile: -13.3350153555612
50th percentile: -9.32205173618002
75th percentile: -6.90195792252087
------------------------------------------------------------
Column: CCa(3)
25th percentile: -13.5427854963858
50th percentile: -8.09705591885503
75th percentile: -5.20825304808952
------------------------------------------------------------
Column: CCa(4)
25th percentile: -6.435699464157643
50th percentile: -3.08783086583838
75th percentile: 2.2303691281043827
------------------------------------------------------------
Column: CCa(5)
25th percentile: -14.176765756826574
50th percentile: -9.96122952035788
75th percentile: -5.185297675292488
------------------------------------------------------------
Column: CCa(6)
25th percentile: -5.86527433257598
50th percentile: -2.38783199387617
75th percentile: 4.870071504791695
------------------------------------------------------------
Column: CCa(7)
25th percentile: -7.793801064498022
50th percentile: -2.96863083577458
75th percentile: 0.28366292179407676
------------------------------------------------------------
Column: CCa(8)
25th percentile: -11.985285024946576
50th percentile: -3.6237166651284998
75th percentile: 2.4546844617393853
------------------------------------------------------------
Column: CCa(9)
25th percentile: -14.332659115127875
50th percentile: -9.67330155148145
75th percentile: -6.368085628208795
------------------------------------------------------------
Column: CCa(10)
25th percentile: -10.248713320033975
50th percentile: -6.80810082751285
75th percentile: -2.2375457684360724
------------------------------------------------------------
Column: CCa(11)
25th percentile: -3.9479222276294275
50th percentile: -0.17363337731558098
75th percentile: 3.569335480096515
------------------------------------------------------------
Column: CCa(12)
25th percentile: -7.029761112787112
50th percentile: -2.56040820246236
75th percentile: 1.769206875225335
------------------------------------------------------------
Column: dCCa(1)
25th percentile: -0.00257184342303265
50th percentile: 0.0013593896801524
75th percentile: 0.0047825222459437
------------------------------------------------------------
Column: dCCa(2)
25th percentile: -0.003071332341382575
50th percentile: 0.0018820322466810498
75th percentile: 0.009408607754847975
------------------------------------------------------------
Column: dCCa(3)
25th percentile: -0.007733629744937175
50th percentile: 0.0025381803714314
75th percentile: 0.009450404081392425
------------------------------------------------------------
Column: dCCa(4)
25th percentile: -0.013452182415116625
50th percentile: 0.0021665622757974998
75th percentile: 0.008980865221360775
------------------------------------------------------------
Column: dCCa(5)
25th percentile: -0.01424902154829445
50th percentile: -0.0028467621848403003
75th percentile: 0.007559349461821
------------------------------------------------------------
Column: dCCa(6)
25th percentile: -0.009356640345190525
50th percentile: 0.0002846530366578015
75th percentile: 0.012157982821948875
------------------------------------------------------------
Column: dCCa(7)
25th percentile: -0.009404712588274824
50th percentile: 0.0002720147648211
75th percentile: 0.016620314400736323
------------------------------------------------------------
Column: dCCa(8)
25th percentile: -0.00947778567445375
50th percentile: -0.00028675110839305
75th percentile: 0.010033658684928075
------------------------------------------------------------
Column: dCCa(9)
25th percentile: -0.005752796165757525
50th percentile: 0.00107466442937245
75th percentile: 0.012329572001504675
------------------------------------------------------------
Column: dCCa(10)
25th percentile: -0.0063323209393362
50th percentile: 0.0008211382892811001
75th percentile: 0.009552092209054574
------------------------------------------------------------
Column: dCCa(11)
25th percentile: -0.010918244048472251
50th percentile: -0.00216183012278145
75th percentile: 0.011213763766791776
------------------------------------------------------------
Column: dCCa(12)
25th percentile: -0.01451867396946865
50th percentile: -0.000695255207532475
75th percentile: 0.009295785908126375
------------------------------------------------------------
Column: J1_i
25th percentile: 0.27266734353801825
50th percentile: 0.409592665633141
75th percentile: 0.6608057624880898
------------------------------------------------------------
Column: J3_i
25th percentile: 0.13155723401397548
50th percentile: 0.239542352443969
75th percentile: 0.36825103480391674
------------------------------------------------------------
Column: J5_i
25th percentile: 0.1790028720991715
50th percentile: 0.250096357259676
75th percentile: 0.39038884017474224
------------------------------------------------------------
Column: J55_i
25th percentile: 0.4399317102875772
50th percentile: 0.65217639717526
75th percentile: 0.9339753805871527
------------------------------------------------------------
Column: S1_i
25th percentile: 1.8798760361809574
50th percentile: 3.038640359546985
75th percentile: 4.258364666010605
------------------------------------------------------------
Column: S3_i
25th percentile: 0.8210334308023168
50th percentile: 1.501306072190735
75th percentile: 2.22790804377745
------------------------------------------------------------
Column: S5_i
25th percentile: 1.061090425701525
50th percentile: 1.741859041268605
75th percentile: 2.5863687873788925
------------------------------------------------------------
Column: S11_i
25th percentile: 1.6964555146544775
50th percentile: 2.762957778780125
75th percentile: 3.6805315432868175
------------------------------------------------------------
Column: S55_i
25th percentile: 3.8646628520491424
50th percentile: 5.664012633667475
75th percentile: 7.2850739804282245
------------------------------------------------------------
Column: DPF_i
25th percentile: 57.38114444820317
50th percentile: 66.3056486654252
75th percentile: 74.47718995591337
------------------------------------------------------------
Column: PFR_i
25th percentile: 0.09449339722271746
50th percentile: 0.153203222692541
75th percentile: 0.271037682274131
------------------------------------------------------------
Column: PPE_i
25th percentile: 0.8376680256099143
50th percentile: 1.1108049886920601
75th percentile: 1.648538532384185
------------------------------------------------------------
Column: PVI_i
25th percentile: 0.25335352504673153
50th percentile: 0.345564494313491
75th percentile: 0.5974841529829752
------------------------------------------------------------
Column: HNR_i
25th percentile: 16.5025463108473
50th percentile: 19.6455688390174
75th percentile: 23.536992380870828
------------------------------------------------------------
Column: GNEi_{\mu}
25th percentile: 0.8735582272273588
50th percentile: 0.8935926491051875
75th percentile: 0.9239990701883013
------------------------------------------------------------
Column: GNEi_{\sigma}
25th percentile: 0.026548225345923625
50th percentile: 0.03013394793714435
75th percentile: 0.032731181223993974
------------------------------------------------------------
Column: Hi(1)_{mu}
25th percentile: -12.084484540985224
50th percentile: -6.448450405224936
75th percentile: -3.384795779815543
------------------------------------------------------------
Column: Hi(2)_{mu}
25th percentile: -13.653747122357675
50th percentile: -7.731010481295515
75th percentile: -4.79325676128857
------------------------------------------------------------
Column: Hi(3)_{mu}
25th percentile: -33.8099473511083
50th percentile: -24.31117035262465
75th percentile: -15.963307046846499
------------------------------------------------------------
Column: Hi(4)_{mu}
25th percentile: -51.497085063213824
50th percentile: -38.7282462994452
75th percentile: -23.792347778141576
------------------------------------------------------------
Column: Hi(5)_{mu}
25th percentile: -56.29215173407705
50th percentile: -49.023876642299996
75th percentile: -33.27537900299963
------------------------------------------------------------
Column: Hi(6)_{mu}
25th percentile: -60.1565064664372
50th percentile: -51.8678002938737
75th percentile: -40.22771232615655
------------------------------------------------------------
Column: Hi(7)_{mu}
25th percentile: -60.19695609560973
50th percentile: -52.61817584350215
75th percentile: -46.0223392040385
------------------------------------------------------------
Column: Hi(8)_{mu}
25th percentile: -61.624319249837654
50th percentile: -53.78321737363075
75th percentile: -47.21013297204985
------------------------------------------------------------
Column: Hi(1)_{sd}
25th percentile: 0.9778358060194745
50th percentile: 1.478875219411755
75th percentile: 2.8274001553729926
------------------------------------------------------------
Column: Hi(2)_{sd}
25th percentile: 1.2664178055371025
50th percentile: 1.993862413478575
75th percentile: 3.8654780727618476
------------------------------------------------------------
Column: Hi(3)_{sd}
25th percentile: 1.7966097560226
50th percentile: 2.8937174183351653
75th percentile: 5.3856838565503775
------------------------------------------------------------
Column: Hi(4)_{sd}
25th percentile: 1.9599379189995374
50th percentile: 3.60069005646755
75th percentile: 5.909224825130121
------------------------------------------------------------
Column: Hi(5)_{sd}
25th percentile: 2.759132047458553
50th percentile: 4.904823335455495
75th percentile: 6.239803533936727
------------------------------------------------------------
Column: Hi(6)_{sd}
25th percentile: 3.431319730673793
50th percentile: 5.059508548367075
75th percentile: 6.132833259067275
------------------------------------------------------------
Column: Hi(7)_{sd}
25th percentile: 3.547628975884135
50th percentile: 5.230949703686726
75th percentile: 6.1056840955266996
------------------------------------------------------------
Column: Hi(8)_{sd}
25th percentile: 3.4311708106298324
50th percentile: 5.354842795336035
75th percentile: 6.409140629784815
------------------------------------------------------------
Column: Hi(1)_{rel}
25th percentile: 0.06281818901579318
50th percentile: 0.1143873113489965
75th percentile: 0.210213284488582
------------------------------------------------------------
Column: Hi(2)_{rel}
25th percentile: 0.051391951772483374
50th percentile: 0.1053242656634035
75th percentile: 0.15123312837977027
------------------------------------------------------------
Column: Hi(3)_{rel}
25th percentile: 0.0266450703634055
50th percentile: 0.035653456349457296
75th percentile: 0.05366315776977047
------------------------------------------------------------
Column: Hi(4)_{rel}
25th percentile: 0.01786372376492465
50th percentile: 0.02387658387706415
75th percentile: 0.03703348118283285
------------------------------------------------------------
Column: Hi(5)_{rel}
25th percentile: 0.015986048774129926
50th percentile: 0.0190254233578682
75th percentile: 0.026545681004683377
------------------------------------------------------------
Column: Hi(6)_{rel}
25th percentile: 0.015163494272782348
50th percentile: 0.017316686664040598
75th percentile: 0.022451171850427977
------------------------------------------------------------
Column: Hi(7)_{rel}
25th percentile: 0.0151356729620395
50th percentile: 0.0172237182123486
75th percentile: 0.019832252469677028
------------------------------------------------------------
Column: Hi(8)_{rel}
25th percentile: 0.01475417592078155
50th percentile: 0.017086554806409998
75th percentile: 0.01982734387646465
------------------------------------------------------------
Column: CCi(1)
25th percentile: -3.2860815264595176
50th percentile: -0.214981618056296
75th percentile: 3.5806901783547125
------------------------------------------------------------
Column: CCi(2)
25th percentile: 8.459895098526498
50th percentile: 14.9648265736674
75th percentile: 18.525066760286276
------------------------------------------------------------
Column: CCi(3)
25th percentile: -10.988101302162626
50th percentile: -7.0211352388377
75th percentile: 1.9682239276501452
------------------------------------------------------------
Column: CCi(4)
25th percentile: -22.068165891934825
50th percentile: -15.20291899790905
75th percentile: -12.892236700689924
------------------------------------------------------------
Column: CCi(5)
25th percentile: -15.78693114540795
50th percentile: -11.798025444099
75th percentile: -5.110792044931733
------------------------------------------------------------
Column: CCi(6)
25th percentile: -15.187249665171176
50th percentile: -8.50913829146116
75th percentile: -1.5574253819684407
------------------------------------------------------------
Column: CCi(7)
25th percentile: -13.059185823518224
50th percentile: -9.522928682647105
75th percentile: -4.26486530111189
------------------------------------------------------------
Column: CCi(8)
25th percentile: -8.654436774203303
50th percentile: -2.77579978295039
75th percentile: 1.4490698271572076
------------------------------------------------------------
Column: CCi(9)
25th percentile: -9.945968305632848
50th percentile: -5.91153800842179
75th percentile: -0.860310309921836
------------------------------------------------------------
Column: CCi(10)
25th percentile: -10.678581036346024
50th percentile: -5.959028902429595
75th percentile: -1.8872778543747053
------------------------------------------------------------
Column: CCi(11)
25th percentile: -6.5770388292702195
50th percentile: -0.7005139646595719
75th percentile: 5.249738260768145
------------------------------------------------------------
Column: CCi(12)
25th percentile: -7.653717636135407
50th percentile: -3.981218915330655
75th percentile: -0.14677327712708127
------------------------------------------------------------
Column: dCCi(1)
25th percentile: -0.001935985492527775
50th percentile: 0.0021869583952064
75th percentile: 0.007794764140436601
------------------------------------------------------------
Column: dCCi(2)
25th percentile: -0.011936216742606351
50th percentile: -0.0050355580822182
75th percentile: 0.00385244087268105
------------------------------------------------------------
Column: dCCi(3)
25th percentile: -0.00382628930890845
50th percentile: 0.00388502563516655
75th percentile: 0.011846558470639725
------------------------------------------------------------
Column: dCCi(4)
25th percentile: -0.016397910890344376
50th percentile: -0.0086701849757265
75th percentile: 0.0037018585594575746
------------------------------------------------------------
Column: dCCi(5)
25th percentile: -0.0033482085580949497
50th percentile: 0.00571360850531565
75th percentile: 0.012577267572504575
------------------------------------------------------------
Column: dCCi(6)
25th percentile: -0.005069709264230525
50th percentile: 0.0041963954695323005
75th percentile: 0.015263198647101
------------------------------------------------------------
Column: dCCi(7)
25th percentile: -0.008233781756475326
50th percentile: 0.00077499537841575
75th percentile: 0.011560906754643425
------------------------------------------------------------
Column: dCCi(8)
25th percentile: -0.004222453852156175
50th percentile: 0.0062718688367585495
75th percentile: 0.019092084106021673
------------------------------------------------------------
Column: dCCi(9)
25th percentile: -0.009654421632963324
50th percentile: -0.0009369107978619
75th percentile: 0.006744794723751651
------------------------------------------------------------
Column: dCCi(10)
25th percentile: -0.009852540779729375
50th percentile: -0.0016141832342591
75th percentile: 0.008348817232383524
------------------------------------------------------------
Column: dCCi(11)
25th percentile: -0.0126736976992679
50th percentile: -0.0039213078573344
75th percentile: 0.00557787604494085
------------------------------------------------------------
Column: dCCi(12)
25th percentile: -0.006534279724802125
50th percentile: 0.000660656714138
75th percentile: 0.0095151562517963
------------------------------------------------------------
Column: d_1
25th percentile: 7.6047335088857295
50th percentile: 9.64656365105407
75th percentile: 10.75752211918045
------------------------------------------------------------
Column: F2_i
25th percentile: 2051.62744745824
50th percentile: 2471.09722222222
75th percentile: 2938.2365603193725
------------------------------------------------------------
Column: F2_{conv}
25th percentile: 800.1811555532445
50th percentile: 1206.596083012105
75th percentile: 1551.6776777613075
------------------------------------------------------------
Column: Diagnosis (ALS)
25th percentile: 0.0
50th percentile: 0.0
75th percentile: 1.0
------------------------------------------------------------

num_col = data.select_dtypes(include='number').columns
num_col= data[num_col].corr()

num_col.head(50)

	ID	Sex	Age	J1_a	J3_a	J5_a	J55_a	S1_a	S3_a	S5_a	...	dCCi(7)	dCCi(8)	dCCi(9)	dCCi(10)	dCCi(11)	dCCi(12)	d_1	F2_i	F2_{conv}	Diagnosis (ALS)
ID	1.000000	0.353321	0.157990	-0.026373	-0.004840	-0.009701	-0.061827	0.154836	0.119789	0.144409	...	0.208685	0.071176	-0.347256	-0.089037	0.247796	-0.050887	0.233082	0.189143	0.263867	-0.303773
Sex	0.353321	1.000000	0.026966	-0.149556	-0.128604	-0.126798	-0.214536	-0.188013	-0.203108	-0.177840	...	-0.162550	-0.005526	-0.198712	-0.109606	0.163906	-0.035527	0.167690	0.340356	0.306281	-0.154675
Age	0.157990	0.026966	1.000000	0.051973	0.038240	0.052406	0.184253	0.084882	0.061959	0.092237	...	-0.134165	-0.189492	0.183250	0.091320	0.109102	0.149704	-0.241235	-0.292623	-0.341549	0.268348
J1_a	-0.026373	-0.149556	0.051973	1.000000	0.995834	0.994075	0.867935	0.882995	0.899598	0.878815	...	-0.052928	0.029363	0.212950	0.054432	0.139971	-0.032022	-0.114077	-0.070777	-0.099791	0.238028
J3_a	-0.004840	-0.128604	0.038240	0.995834	1.000000	0.988530	0.839019	0.879236	0.899346	0.872188	...	-0.045487	0.038137	0.212400	0.063660	0.170578	-0.021323	-0.079288	-0.044397	-0.082155	0.208204
J5_a	-0.009701	-0.126798	0.052406	0.994075	0.988530	1.000000	0.874643	0.893664	0.908115	0.892783	...	-0.046432	0.013669	0.200544	0.041891	0.144866	-0.021428	-0.132156	-0.079135	-0.111213	0.243350
J55_a	-0.061827	-0.214536	0.184253	0.867935	0.839019	0.874643	1.000000	0.797444	0.789768	0.790186	...	-0.086021	0.009768	0.180152	-0.024549	0.012061	-0.092113	-0.211375	-0.182878	-0.209832	0.361060
S1_a	0.154836	-0.188013	0.084882	0.882995	0.879236	0.893664	0.797444	1.000000	0.990674	0.995822	...	0.064927	-0.011202	0.134377	-0.012836	0.103668	-0.092436	-0.128456	-0.080673	-0.094561	0.207148
S3_a	0.119789	-0.203108	0.061959	0.899598	0.899346	0.908115	0.789768	0.990674	1.000000	0.984072	...	0.042930	0.007786	0.162742	-0.001081	0.103876	-0.108362	-0.130162	-0.076678	-0.095897	0.177238
S5_a	0.144409	-0.177840	0.092237	0.878815	0.872188	0.892783	0.790186	0.995822	0.984072	1.000000	...	0.075518	-0.025746	0.136396	-0.021068	0.097010	-0.082632	-0.140389	-0.084438	-0.095618	0.216198
S11_a	0.205873	-0.145135	0.118785	0.793785	0.783033	0.805991	0.753024	0.960532	0.916210	0.961339	...	0.060978	-0.057123	0.073671	-0.027948	0.088130	-0.073699	-0.129973	-0.067801	-0.069646	0.261048
S55_a	0.383137	-0.002084	0.171393	0.515869	0.514877	0.532364	0.541721	0.739277	0.663008	0.722471	...	0.028009	-0.140707	-0.086882	-0.106442	0.238431	0.026470	-0.092864	-0.053242	-0.086201	0.208952
DPF_a	0.052248	-0.108992	-0.119327	0.288830	0.325792	0.247616	0.067845	0.344221	0.355784	0.322806	...	0.042554	0.049755	0.090050	0.042907	0.190135	0.017491	0.090162	0.160099	0.119822	-0.053611
PFR_a	-0.153993	-0.163947	0.136052	0.690337	0.678378	0.720521	0.817227	0.648049	0.643953	0.631902	...	-0.137935	-0.081020	0.233789	0.153089	-0.074192	-0.092107	-0.285186	-0.295120	-0.331199	0.346148
PPE_a	-0.130256	-0.232222	0.049242	0.765816	0.728855	0.740427	0.763242	0.708401	0.684491	0.705889	...	-0.020192	-0.009301	0.088148	-0.033811	0.040789	0.016792	-0.189690	-0.086289	-0.068347	0.417961
PVI_a	-0.189488	-0.220055	0.154727	0.791731	0.746664	0.804099	0.861297	0.665946	0.674429	0.674411	...	-0.067540	-0.034525	0.268916	0.012992	-0.116683	-0.057818	-0.261770	-0.194039	-0.209879	0.422460
HNR_a	-0.115708	0.271977	-0.237268	-0.428649	-0.384898	-0.411513	-0.580829	-0.559603	-0.513468	-0.562368	...	-0.034511	0.137268	-0.088407	0.032703	0.206220	0.176200	0.115798	0.280842	0.206106	-0.274830
GNEa_{\mu}	-0.274318	0.156837	-0.175086	-0.377819	-0.369656	-0.383533	-0.355030	-0.486878	-0.445019	-0.470542	...	-0.171565	0.033288	0.037797	0.095623	-0.272991	-0.186209	0.262516	0.165956	0.128041	-0.323897
GNEa_{\sigma}	0.119406	-0.091656	0.119993	0.294581	0.275977	0.295543	0.289110	0.312284	0.294989	0.306613	...	0.062595	-0.128362	-0.052642	-0.120171	0.152256	0.084637	-0.319755	-0.175092	-0.114885	0.192111
Ha(1)_{mu}	-0.165213	0.219942	-0.077950	-0.100325	-0.081962	-0.109162	-0.281462	-0.299688	-0.238590	-0.287443	...	-0.048923	0.097181	0.025277	0.058374	0.231784	0.316713	-0.090537	0.348738	0.151358	-0.015091
Ha(2)_{mu}	-0.124926	0.153188	-0.008920	-0.333848	-0.338693	-0.345808	-0.334003	-0.467480	-0.416931	-0.466933	...	-0.105838	0.142852	0.069376	0.082776	-0.259809	-0.272162	0.081067	0.059393	0.125689	-0.103948
Ha(3)_{mu}	0.103856	0.042970	0.132355	-0.324382	-0.305862	-0.337177	-0.265084	-0.335657	-0.324664	-0.350520	...	-0.171801	-0.067974	0.139057	0.009668	0.143731	0.160939	0.116345	0.301187	0.074147	-0.053245
Ha(4)_{mu}	-0.353378	-0.281106	-0.119140	-0.306979	-0.317505	-0.344421	-0.251308	-0.379710	-0.351397	-0.381840	...	-0.044602	0.146047	0.038474	0.039402	-0.433339	-0.299241	-0.015277	-0.138041	-0.068521	-0.117915
Ha(5)_{mu}	-0.118375	-0.292146	-0.086949	-0.245845	-0.253985	-0.262802	-0.276637	-0.214620	-0.189395	-0.216307	...	0.032909	0.129864	0.082642	0.179189	-0.427686	-0.134016	0.063195	-0.003550	-0.019178	-0.146109
Ha(6)_{mu}	-0.079265	-0.173885	-0.058820	-0.237309	-0.248495	-0.253947	-0.213256	-0.165262	-0.145764	-0.161030	...	-0.105779	0.125878	0.153285	0.201286	-0.438548	-0.385522	0.153544	-0.075255	0.005604	-0.225711
Ha(7)_{mu}	-0.138349	-0.250066	-0.155735	-0.186489	-0.199179	-0.219710	-0.179375	-0.174628	-0.164442	-0.171237	...	0.017939	0.156912	0.029694	0.334677	-0.339610	-0.163573	0.266243	0.228675	0.204882	-0.320722
Ha(8)_{mu}	-0.121539	-0.287087	-0.104500	-0.237183	-0.255279	-0.269171	-0.179818	-0.196300	-0.185822	-0.195528	...	0.101023	0.116308	-0.019787	0.199782	-0.339482	-0.237434	0.138015	0.039868	0.093605	-0.334755
Ha(1)_{sd}	0.252948	0.102299	0.155345	0.209805	0.210181	0.226781	0.335070	0.359836	0.298946	0.334164	...	0.015322	-0.091236	-0.018951	-0.002015	0.054685	-0.028832	-0.143228	-0.222298	-0.130551	0.190342
Ha(2)_{sd}	0.367868	0.037722	0.248074	0.333632	0.337597	0.351552	0.379341	0.484586	0.421893	0.462802	...	0.154778	-0.222537	0.038520	-0.126101	0.205058	0.220975	-0.139401	-0.071389	-0.113314	0.212794
Ha(3)_{sd}	0.309049	0.211195	0.156628	0.304801	0.309094	0.320883	0.351082	0.386050	0.349712	0.369003	...	0.018762	0.048072	-0.075712	-0.048224	0.061917	-0.229973	-0.093331	-0.315621	-0.106607	0.115198
Ha(4)_{sd}	0.370749	0.165470	0.236827	0.387895	0.400588	0.401136	0.341038	0.487909	0.433998	0.482561	...	0.101399	0.015593	0.044674	-0.125766	0.394360	0.183781	0.013436	0.102594	-0.017495	0.282022
Ha(5)_{sd}	0.190336	0.143987	0.165381	0.157068	0.146388	0.163994	0.291736	0.252341	0.187416	0.245967	...	0.170039	0.007895	-0.183807	-0.270519	0.069701	-0.192071	-0.041828	-0.229205	-0.039539	0.139464
Ha(6)_{sd}	0.155783	-0.020445	0.190076	0.694273	0.698622	0.708815	0.667639	0.700282	0.679247	0.685565	...	0.153504	-0.007750	0.019564	-0.113980	0.243379	0.089395	-0.234560	-0.083438	-0.157219	0.254764
Ha(7)_{sd}	0.052744	-0.013304	0.186713	0.698752	0.690979	0.721400	0.669413	0.663441	0.649653	0.657755	...	0.064100	-0.040921	0.035504	-0.096022	0.154848	-0.059691	-0.284878	-0.297898	-0.199028	0.332693
Ha(8)_{sd}	0.141361	0.067460	0.178830	0.569665	0.583398	0.591051	0.525456	0.593315	0.574077	0.577069	...	0.102714	-0.049433	0.125121	-0.040302	0.210561	0.119946	-0.163228	-0.077791	-0.155392	0.264278
Ha(1)_{rel}	-0.095473	0.314179	-0.148719	-0.146390	-0.129113	-0.146499	-0.236918	-0.291669	-0.244440	-0.282002	...	-0.096597	0.085456	0.119435	0.116045	0.087542	0.112024	0.012699	0.383269	0.290716	-0.284783
Ha(2)_{rel}	-0.073710	0.234598	-0.119944	-0.294257	-0.302698	-0.294785	-0.303144	-0.413920	-0.372068	-0.405555	...	-0.132439	0.083500	0.009204	0.057287	-0.240436	-0.287788	0.076844	0.056267	0.183504	-0.133741
Ha(3)_{rel}	0.018118	0.034654	0.105481	-0.234632	-0.225401	-0.238622	-0.211914	-0.266072	-0.248613	-0.264441	...	-0.147224	-0.030036	-0.000487	-0.012761	-0.034920	-0.073090	0.054324	0.113177	0.054053	-0.053738
Ha(4)_{rel}	-0.312251	-0.192759	-0.181772	-0.176872	-0.171464	-0.212065	-0.173253	-0.296860	-0.274823	-0.302252	...	-0.077421	0.080714	-0.003491	0.073038	-0.387192	-0.256537	0.169031	-0.178908	-0.077074	-0.218108
Ha(5)_{rel}	-0.165205	-0.353902	-0.021493	-0.182862	-0.191071	-0.188883	-0.217096	-0.173020	-0.149401	-0.165262	...	-0.052315	0.105585	0.130669	0.154075	-0.242847	0.003731	0.070306	-0.083541	-0.055161	-0.041251
Ha(6)_{rel}	-0.110018	-0.227471	-0.050297	-0.188892	-0.194743	-0.196042	-0.204503	-0.120517	-0.087448	-0.119130	...	-0.028841	0.092112	0.073490	0.049186	-0.254953	-0.211050	0.139718	-0.127795	0.020468	-0.189111
Ha(7)_{rel}	-0.022862	-0.133471	-0.197313	-0.198924	-0.203528	-0.219053	-0.195084	-0.159736	-0.143192	-0.159307	...	0.011541	0.113847	0.052455	0.287087	-0.242712	-0.066287	0.359289	0.277678	0.285354	-0.380733
Ha(8)_{rel}	-0.027698	-0.186962	-0.061388	-0.154618	-0.174363	-0.174539	-0.086011	-0.105243	-0.094455	-0.103247	...	0.058278	0.042485	-0.035928	0.128137	-0.141871	-0.133814	0.218747	0.113280	0.156139	-0.306330
CCa(1)	-0.353817	-0.303120	-0.152466	0.252826	0.237095	0.219133	0.174051	0.051070	0.087604	0.034774	...	-0.033666	0.124004	0.131498	0.098664	-0.086036	0.051651	-0.122458	-0.125364	-0.035485	0.175759
CCa(2)	-0.266577	-0.408539	-0.099429	-0.030422	-0.050068	-0.055066	-0.067482	-0.064553	-0.060828	-0.062895	...	0.040505	0.093730	-0.045816	-0.031705	0.105632	0.047969	-0.462804	-0.128311	-0.084633	0.171248
CCa(3)	-0.346831	0.019501	-0.065033	0.321444	0.297212	0.315099	0.261434	0.168076	0.181808	0.176193	...	-0.095471	0.003133	0.100248	0.216155	-0.222838	-0.050155	-0.107038	-0.089647	-0.167311	0.061485
CCa(4)	0.155517	0.095044	-0.187602	0.046988	0.047891	0.046431	-0.066258	0.074781	0.093887	0.085955	...	0.080663	0.065329	0.084401	-0.005626	0.167466	0.068991	0.033302	0.359973	0.350782	-0.042322
CCa(5)	-0.074996	-0.396060	0.025439	0.003738	0.004417	-0.016510	0.019357	0.101384	0.101657	0.095316	...	0.056309	0.248074	-0.124367	0.203504	-0.143172	-0.061662	0.129597	-0.087925	-0.012165	-0.176212
CCa(6)	-0.280783	-0.318111	-0.006001	0.267408	0.232462	0.245960	0.306436	0.112808	0.109729	0.108521	...	-0.058603	-0.000882	0.072978	0.135972	-0.077029	0.001647	-0.212419	-0.223360	-0.210412	0.302913
CCa(7)	-0.194842	-0.555857	-0.109585	0.043656	0.035913	0.020235	0.174208	0.121908	0.104421	0.098404	...	0.036690	0.004805	-0.076866	0.063435	-0.151985	-0.138205	-0.164200	-0.186883	-0.152395	0.138397

50 rows × 135 columns

plt.figure (figsize=(70,70))
sns.heatmap(data=num_col,annot=True,fmt='.2g',cmap='viridis')
#plt.tight_layout()

<AxesSubplot:>

corr_matrix=data.corr()

corr_matrix

	ID	Sex	Age	J1_a	J3_a	J5_a	J55_a	S1_a	S3_a	S5_a	...	dCCi(7)	dCCi(8)	dCCi(9)	dCCi(10)	dCCi(11)	dCCi(12)	d_1	F2_i	F2_{conv}	Diagnosis (ALS)
ID	1.000000	0.353321	0.157990	-0.026373	-0.004840	-0.009701	-0.061827	0.154836	0.119789	0.144409	...	0.208685	0.071176	-0.347256	-0.089037	0.247796	-0.050887	0.233082	0.189143	0.263867	-0.303773
Sex	0.353321	1.000000	0.026966	-0.149556	-0.128604	-0.126798	-0.214536	-0.188013	-0.203108	-0.177840	...	-0.162550	-0.005526	-0.198712	-0.109606	0.163906	-0.035527	0.167690	0.340356	0.306281	-0.154675
Age	0.157990	0.026966	1.000000	0.051973	0.038240	0.052406	0.184253	0.084882	0.061959	0.092237	...	-0.134165	-0.189492	0.183250	0.091320	0.109102	0.149704	-0.241235	-0.292623	-0.341549	0.268348
J1_a	-0.026373	-0.149556	0.051973	1.000000	0.995834	0.994075	0.867935	0.882995	0.899598	0.878815	...	-0.052928	0.029363	0.212950	0.054432	0.139971	-0.032022	-0.114077	-0.070777	-0.099791	0.238028
J3_a	-0.004840	-0.128604	0.038240	0.995834	1.000000	0.988530	0.839019	0.879236	0.899346	0.872188	...	-0.045487	0.038137	0.212400	0.063660	0.170578	-0.021323	-0.079288	-0.044397	-0.082155	0.208204
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
dCCi(12)	-0.050887	-0.035527	0.149704	-0.032022	-0.021323	-0.021428	-0.092113	-0.092436	-0.108362	-0.082632	...	0.173764	-0.343383	0.157203	-0.132449	0.470498	1.000000	-0.061372	0.107644	-0.111535	0.236204
d_1	0.233082	0.167690	-0.241235	-0.114077	-0.079288	-0.132156	-0.211375	-0.128456	-0.130162	-0.140389	...	0.187991	0.321498	-0.089471	0.126085	-0.081252	-0.061372	1.000000	0.465199	0.498940	-0.456277
F2_i	0.189143	0.340356	-0.292623	-0.070777	-0.044397	-0.079135	-0.182878	-0.080673	-0.076678	-0.084438	...	0.113125	0.268072	-0.154876	0.009993	0.200750	0.107644	0.465199	1.000000	0.850953	-0.302416
F2_{conv}	0.263867	0.306281	-0.341549	-0.099791	-0.082155	-0.111213	-0.209832	-0.094561	-0.095897	-0.095618	...	0.189239	0.344863	-0.274540	-0.031831	0.084229	-0.111535	0.498940	0.850953	1.000000	-0.390466
Diagnosis (ALS)	-0.303773	-0.154675	0.268348	0.238028	0.208204	0.243350	0.361060	0.207148	0.177238	0.216198	...	-0.131194	-0.257660	0.237359	-0.183486	0.023006	0.236204	-0.456277	-0.302416	-0.390466	1.000000

135 rows × 135 columns

corr_matrix['Diagnosis (ALS)']

ID                -0.303773
Sex               -0.154675
Age                0.268348
J1_a               0.238028
J3_a               0.208204
                     ...   
dCCi(12)           0.236204
d_1               -0.456277
F2_i              -0.302416
F2_{conv}         -0.390466
Diagnosis (ALS)    1.000000
Name: Diagnosis (ALS), Length: 135, dtype: float64

sns.pairplot(data=data.iloc[:,2:10])

<seaborn.axisgrid.PairGrid at 0x19ff8c31ca0>

from sklearn import tree
from sklearn.svm import SVC
from sklearn.compose import ColumnTransformer
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import  Pipeline
from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.model_selection import train_test_split,GridSearchCV,RandomizedSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold,StratifiedKFold,LeaveOneOut,ShuffleSplit
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier,BaggingClassifier,AdaBoostClassifier,GradientBoostingClassifier
from sklearn.metrics import accuracy_score,classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.multiclass import OneVsRestClassifier

del data['ID']

x= data.drop('Diagnosis (ALS)',axis = 1)
y= data['Diagnosis (ALS)']

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)

model = [
    DecisionTreeClassifier(),
    RandomForestClassifier(),
    GradientBoostingClassifier(),
    BaggingClassifier(),
    AdaBoostClassifier(),
    LogisticRegression(),
    SVC(),
    KNeighborsClassifier(),
    GaussianNB()
]

x_train

	Sex	Age	J1_a	J3_a	J5_a	J55_a	S1_a	S3_a	S5_a	S11_a	...	dCCi(6)	dCCi(7)	dCCi(8)	dCCi(9)	dCCi(10)	dCCi(11)	dCCi(12)	d_1	F2_i	F2_{conv}
13	1	63	0.805433	0.335434	0.487871	1.813700	4.485662	2.192828	2.864096	4.283792	...	0.005770	-0.028306	-0.035823	-0.004287	-0.014985	-0.004156	-0.005061	7.029500	2730.764545	1288.920905
34	0	38	0.415366	0.254119	0.243906	0.449362	2.394697	1.292778	1.468358	1.860062	...	0.000016	0.008047	-0.005823	-0.006588	-0.000030	0.003137	-0.002440	8.531677	3515.050257	2441.219054
50	1	57	0.098881	0.065791	0.092655	0.363699	0.883453	0.449099	0.497111	0.804179	...	0.015152	-0.027019	0.037807	-0.019812	0.000436	-0.002840	-0.013161	10.108459	2143.018556	805.694015
3	1	70	0.455793	0.174870	0.243660	0.962641	2.883768	1.284926	1.915058	2.929953	...	0.013213	-0.019285	-0.021768	0.020495	0.035976	-0.034648	0.008021	5.447137	1860.172768	359.409974
17	0	57	0.351191	0.165077	0.227554	0.849025	3.695872	1.776465	2.379529	3.718969	...	0.024865	-0.008019	0.005046	0.023446	-0.000726	-0.016418	0.010174	6.031056	2074.091402	927.063276
49	1	39	0.463874	0.290374	0.278926	0.550290	2.449028	1.372602	1.412486	1.786304	...	-0.000234	0.011520	0.009453	0.002072	-0.002811	0.004577	-0.009058	10.616077	3164.536485	2037.766311
8	0	67	0.537010	0.243234	0.318075	1.951256	6.568645	2.891654	4.620345	7.249579	...	0.014234	0.013594	-0.003722	-0.003859	-0.011985	-0.027240	0.002325	8.760510	1840.022120	669.022078
6	0	57	0.691093	0.406901	0.406287	0.765986	6.168256	3.702088	3.286232	4.352221	...	0.002661	-0.012605	0.013385	0.004513	0.001568	-0.008244	0.005801	5.945219	2313.388825	1219.744513
33	0	41	1.063272	0.683918	0.504427	0.613402	3.176717	1.870164	1.590798	2.089394	...	0.017293	-0.008880	-0.001511	0.003965	0.015240	-0.006500	-0.008723	10.482453	2263.284796	801.333727
4	0	66	0.269335	0.143961	0.167465	0.547745	2.327924	1.164109	1.420891	2.141512	...	0.002948	-0.005743	0.004726	-0.015247	0.003900	-0.007686	-0.003784	8.562517	2051.627447	817.111847
47	1	63	0.753234	0.467912	0.442792	0.540327	4.801768	2.677700	3.241265	3.518265	...	-0.018937	0.011239	0.011488	0.015334	0.008337	0.005474	0.011687	10.515820	3057.463491	1494.054076
19	0	40	0.496922	0.213975	0.293201	0.957065	3.293700	1.672811	1.894214	2.932669	...	0.023989	-0.030137	0.024769	0.000567	0.005071	-0.023693	0.009770	7.872279	2143.018556	985.160918
54	1	62	0.823780	0.503064	0.497916	0.704065	6.861939	3.993216	4.174705	4.703103	...	0.014494	0.003410	0.000716	-0.010791	0.004878	-0.005527	-0.001659	7.388631	2096.808356	737.085571
41	1	37	0.818954	0.484998	0.511515	0.721296	4.942091	2.724820	3.320166	3.752014	...	0.005945	0.021267	-0.008438	0.007339	-0.004838	-0.004133	0.000937	10.151495	2444.009071	1157.993834
55	1	50	0.462076	0.269359	0.296952	0.909990	5.020638	2.305690	2.837745	5.387330	...	0.001699	-0.001674	0.012043	-0.003015	0.022928	-0.005444	-0.006394	14.651111	3515.050257	2210.936432
31	1	64	0.219429	0.144385	0.171661	0.555528	2.054277	1.108746	1.260472	1.580742	...	-0.011558	-0.004168	0.018603	0.019350	0.016342	0.014127	0.022756	10.121803	2988.533127	1332.559788
30	1	53	0.561542	0.331788	0.345130	1.020709	6.074875	2.798090	3.526055	5.735960	...	0.022664	0.008537	-0.000306	-0.012570	-0.048113	0.009073	-0.005201	12.036001	2526.285657	1200.269866
59	0	43	0.255799	0.123679	0.182658	0.505591	6.222031	2.876602	3.894294	5.697601	...	-0.060395	0.220533	0.089766	-0.120838	-0.004221	-0.013165	0.004642	9.855665	3128.341308	1990.937097
60	0	63	0.513175	0.296489	0.334845	0.729804	9.686563	4.327943	5.687977	9.912757	...	-0.046223	0.028016	-0.038739	0.011588	-0.011281	-0.004294	0.011239	11.094558	1964.218942	601.076046
15	0	61	1.177795	0.730069	0.569287	1.542224	5.883227	3.384535	2.875918	4.264450	...	0.028733	-0.004708	-0.004383	0.006398	-0.014412	-0.005085	0.005530	7.148809	2051.627447	784.563460
27	1	52	0.475047	0.308491	0.266091	0.396462	3.686641	1.999494	2.083195	2.985985	...	0.009989	0.000009	0.042347	-0.003170	0.002103	0.011023	-0.031324	11.597077	2792.655884	1457.933269
26	1	55	0.764443	0.423008	0.472764	1.699466	8.254286	3.966819	5.010081	7.932608	...	0.018794	0.036827	0.029426	-0.024056	-0.065735	0.012986	0.012941	9.482607	2471.097222	1019.521207
24	1	55	0.419330	0.255329	0.338319	0.486463	4.366452	1.932596	2.855240	4.193509	...	-0.005714	0.012337	-0.005475	-0.001179	-0.002687	0.005369	0.034289	11.766102	2888.617021	1839.961952
46	1	55	0.454844	0.289458	0.293609	0.582757	2.124786	1.170689	1.182154	1.450585	...	0.006111	0.003214	0.007520	-0.024862	0.016600	-0.015827	0.002088	12.189059	3092.653846	1551.286187
11	0	67	1.608454	0.989100	0.894706	1.304613	6.031953	3.279233	3.729620	4.661445	...	0.019701	0.004293	0.026891	0.080337	-0.007352	0.055993	0.075071	10.674106	2007.441819	481.009629
32	1	53	0.598550	0.350577	0.378646	0.654399	4.224992	2.312947	2.678596	3.073228	...	0.001768	-0.003924	0.001995	0.004019	-0.007207	0.004614	0.003154	7.615608	2921.471038	1230.982918
57	1	65	0.356684	0.233194	0.242305	0.410246	3.251335	1.867689	1.908461	2.207320	...	0.005494	-0.003761	0.006982	0.012774	0.008384	0.002823	-0.014323	10.889792	2471.097222	1549.074209
45	1	60	0.286517	0.176603	0.195712	0.692300	1.847736	0.869278	1.099434	1.878774	...	0.023767	-0.020693	0.019078	-0.034556	-0.006731	-0.025442	-0.005668	12.874560	2670.464441	1309.871125
37	0	35	0.758571	0.464961	0.413086	0.605323	9.271523	5.707370	5.150374	5.898369	...	-0.003686	0.018992	0.034957	0.016890	-0.009376	-0.008998	-0.007465	12.892692	2888.617021	1653.804580
29	0	69	0.511742	0.313666	0.320677	0.431433	7.417198	4.258109	4.961685	5.098505	...	-0.013859	0.000308	-0.038984	0.048771	-0.014493	0.008647	-0.004524	2.512995	1233.583584	48.246203
43	0	52	1.529994	0.894926	0.809959	1.715071	7.627483	4.321696	4.415383	5.571067	...	0.015596	-0.012218	0.015828	-0.014697	0.010636	0.004654	-0.003235	7.758796	2761.507400	1641.852909
48	1	45	0.155762	0.091831	0.117099	0.575170	1.575403	0.738640	0.834830	1.517716	...	0.002348	-0.002313	0.008766	0.006330	0.016645	-0.003417	0.002115	10.713432	3599.554394	2226.127951
1	1	57	0.344026	0.177032	0.206458	0.827714	1.967728	0.856639	1.179851	2.050048	...	0.019235	0.002485	-0.004535	-0.000225	-0.006977	-0.012510	0.014773	5.729322	1985.712014	561.802625
21	0	68	0.379367	0.223560	0.308426	1.821009	5.539475	2.551095	3.000675	5.956155	...	0.008846	-0.006281	-0.004169	-0.008164	0.012667	-0.015442	0.001157	5.218871	1649.621788	177.843734
2	1	58	0.264740	0.148228	0.177078	0.532566	1.850893	0.942743	1.071950	1.502212	...	0.007199	-0.013927	0.007908	0.007960	-0.009022	-0.012488	-0.015588	8.258488	2364.695972	796.723440
62	1	68	1.336216	0.815757	0.733197	0.981928	11.224542	5.295879	6.994751	11.706090	...	0.025679	0.015712	0.013437	0.025113	0.008852	-0.010132	-0.008458	10.670669	3201.250289	2284.051658
39	0	38	0.176448	0.103346	0.123381	0.649644	2.649815	1.410593	1.504265	2.236375	...	0.016461	0.014798	-0.011568	-0.009276	-0.003996	-0.003248	-0.005049	10.074644	2119.782609	580.338238
35	0	60	0.391222	0.200687	0.232286	0.638498	2.324343	1.181015	1.475889	1.993371	...	-0.005405	-0.006063	0.028443	-0.006618	0.003011	-0.005547	-0.002806	10.525555	2700.419449	1393.549002
23	1	63	0.504802	0.253832	0.313823	1.229761	6.571067	2.833840	4.277576	7.448475	...	0.013513	-0.003382	0.027770	-0.005112	-0.027102	0.012612	-0.010165	7.339732	3556.978755	2129.076098
53	1	60	0.121065	0.079473	0.101627	0.285497	1.117740	0.558948	0.615284	0.924619	...	-0.005255	0.017626	-0.007311	-0.006541	-0.012625	0.013987	0.014222	11.261019	3201.250289	1960.299963
10	0	67	1.232990	0.561293	0.784691	1.889785	4.139457	1.915796	2.692091	4.296011	...	0.031624	-0.018089	0.018493	0.006547	-0.004817	-0.003710	-0.000692	2.276702	1686.160000	669.461749
22	1	64	0.437128	0.265519	0.349086	0.493971	3.205305	1.515726	2.119909	2.591283	...	-0.060397	0.056134	-0.069202	-0.040180	-0.058987	0.044552	0.077897	2.986929	1800.351911	784.205580
18	0	57	0.426554	0.202661	0.255198	0.885479	4.387137	1.892587	2.628737	4.550564	...	0.003446	0.010420	0.005288	-0.006713	0.013147	-0.017816	0.017087	11.136041	2238.670803	930.223353
63	1	60	0.916706	0.566121	0.512857	1.467165	6.372832	3.251168	3.539229	5.627211	...	-0.072828	-0.046235	0.041946	-0.065313	-0.016682	0.061026	-0.005883	6.972152	2792.655884	1518.529172
20	1	64	0.797801	0.470116	0.522910	1.663179	7.778152	3.640445	4.355894	7.958491	...	0.028207	-0.052002	-0.038210	0.028143	0.033156	0.010924	0.020823	5.924309	1880.538263	482.819916
7	0	58	1.210548	0.726523	0.661670	1.205596	8.492104	4.109625	5.377768	8.005940	...	0.007534	0.017088	0.015907	-0.006379	0.057303	-0.001919	-0.008007	8.422353	1964.218942	759.068477
42	1	50	0.323175	0.126138	0.176197	0.907850	3.069980	1.372204	1.869070	3.225635	...	0.004152	-0.001733	0.030566	-0.037259	-0.020167	-0.042955	-0.083977	9.167460	444.730268	1169.075556
14	1	62	1.394205	0.839935	0.703224	1.362995	4.661601	2.402909	2.826132	4.168789	...	-0.000823	0.025207	-0.002524	-0.006103	-0.012850	-0.015023	0.019711	12.862700	3092.653846	1552.852150
28	0	68	1.076446	0.624824	0.561100	1.965493	7.001105	3.785627	4.033533	5.810484	...	-0.011602	0.018274	0.004485	0.005101	-0.007367	0.007041	-0.001560	11.097431	3515.050257	2157.871393
51	0	80	0.532330	0.296012	0.322217	0.650045	8.042171	4.754839	5.060159	5.833646	...	-0.014078	0.010696	-0.003448	-0.015049	0.010195	-0.002152	0.001881	9.472404	2238.670803	1241.852695
38	0	60	0.182721	0.089174	0.113216	0.651748	1.839764	0.673977	1.084464	2.096809	...	0.021454	0.007635	0.019134	0.013320	0.002002	-0.022146	0.005825	15.420777	2313.388825	1381.628235

51 rows × 133 columns

y_train

13    1
34    0
50    0
3     1
17    1
49    0
8     1
6     1
33    0
4     1
47    0
19    1
54    0
41    0
55    0
31    0
30    1
59    0
60    0
15    1
27    1
26    1
24    1
46    0
11    1
32    0
57    0
45    0
37    0
29    1
43    0
48    0
1     1
21    1
2     1
62    0
39    0
35    0
23    1
53    0
10    1
22    1
18    1
63    0
20    1
7     1
42    0
14    1
28    1
51    0
38    0
Name: Diagnosis (ALS), dtype: int64

y_test

52    0
58    0
0     1
44    0
5     1
36    0
16    1
12    1
25    1
61    0
56    0
9     1
40    0
Name: Diagnosis (ALS), dtype: int64

KFold

for i in model:
    kfold = KFold(8)
    score = cross_val_score(estimator=i,X=x,y=y,cv=kfold)
    print(i)
    print(f'score:',score)
    print(f'mean_value:',score.mean())
    print(f'min_value:',score.min())
    print('------'*10)

DecisionTreeClassifier()
score: [0.625 0.75  0.75  0.5   0.375 0.625 0.625 0.5  ]
mean_value: 0.59375
min_value: 0.375
------------------------------------------------------------
RandomForestClassifier()
score: [0.625 0.75  0.875 0.375 0.75  0.375 1.    0.125]
mean_value: 0.609375
min_value: 0.125
------------------------------------------------------------
GradientBoostingClassifier()
score: [0.5   0.75  0.875 0.5   0.375 0.625 0.625 0.5  ]
mean_value: 0.59375
min_value: 0.375
------------------------------------------------------------
BaggingClassifier()
score: [0.5   0.625 0.75  0.5   0.5   0.75  0.75  0.5  ]
mean_value: 0.609375
min_value: 0.5
------------------------------------------------------------
AdaBoostClassifier()
score: [0.375 0.875 0.75  0.25  0.875 0.625 0.875 0.375]
mean_value: 0.625
min_value: 0.25
------------------------------------------------------------
LogisticRegression()
score: [0.875 0.75  0.875 0.25  0.625 0.625 0.75  0.625]
mean_value: 0.671875
min_value: 0.25
------------------------------------------------------------
SVC()
score: [0.25  0.5   0.375 0.25  0.625 0.625 0.625 0.5  ]
mean_value: 0.46875
min_value: 0.25
------------------------------------------------------------
KNeighborsClassifier()
score: [0.375 0.5   0.875 0.25  0.5   0.375 0.75  0.5  ]
mean_value: 0.515625
min_value: 0.25
------------------------------------------------------------
GaussianNB()
score: [0.375 0.625 0.625 0.5   0.875 0.75  1.    0.125]
mean_value: 0.609375
min_value: 0.125
------------------------------------------------------------

StratifiedKFold

for i in model:
    skf= StratifiedKFold(5)
    score2 = cross_val_score(estimator=i,X=x,y=y,cv=skf)
    print(i)
    print(f'score:',score2)
    print(f'mean_value:',score2.mean())
    print(f'min_value:',score2.min())
    print('------'*10)

DecisionTreeClassifier()
score: [0.46153846 0.61538462 1.         0.92307692 0.58333333]
mean_value: 0.7166666666666667
min_value: 0.46153846153846156
------------------------------------------------------------
RandomForestClassifier()
score: [0.76923077 0.69230769 0.76923077 0.84615385 0.5       ]
mean_value: 0.7153846153846154
min_value: 0.5
------------------------------------------------------------
GradientBoostingClassifier()
score: [0.76923077 0.53846154 0.84615385 0.92307692 0.66666667]
mean_value: 0.7487179487179486
min_value: 0.5384615384615384
------------------------------------------------------------
BaggingClassifier()
score: [0.61538462 0.76923077 0.92307692 0.84615385 0.75      ]
mean_value: 0.7807692307692308
min_value: 0.6153846153846154
------------------------------------------------------------
AdaBoostClassifier()
score: [0.76923077 0.69230769 0.92307692 0.76923077 0.66666667]
mean_value: 0.7641025641025642
min_value: 0.6666666666666666
------------------------------------------------------------
LogisticRegression()
score: [0.76923077 0.84615385 0.76923077 0.69230769 0.5       ]
mean_value: 0.7153846153846153
min_value: 0.5
------------------------------------------------------------
SVC()
score: [0.84615385 0.53846154 0.76923077 0.61538462 0.5       ]
mean_value: 0.6538461538461539
min_value: 0.5
------------------------------------------------------------
KNeighborsClassifier()
score: [0.46153846 0.46153846 0.69230769 0.69230769 0.5       ]
mean_value: 0.5615384615384615
min_value: 0.46153846153846156
------------------------------------------------------------
GaussianNB()
score: [0.69230769 0.69230769 0.84615385 0.84615385 0.5       ]
mean_value: 0.7153846153846154
min_value: 0.5
------------------------------------------------------------

LeaveOneOut

LOO = LeaveOneOut()
for i  in  model:
    score3 = cross_val_score(estimator=i ,X=x,y=y,cv=LOO)
    print(i)
    print(f'score:',score3)
    print(f'mean_value:',score3.mean())
    print('------'*10)

DecisionTreeClassifier()
score: [1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 1.
 1. 1. 1. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 1.
 1. 1. 0. 1. 0. 0. 1. 1. 0. 1. 1. 0. 1. 1. 0. 0.]
mean_value: 0.609375
------------------------------------------------------------
RandomForestClassifier()
score: [1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0.
 0. 1. 1. 0. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0.]
mean_value: 0.71875
------------------------------------------------------------
GradientBoostingClassifier()
score: [1. 1. 1. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 1.
 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1.
 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 0.]
mean_value: 0.75
------------------------------------------------------------
BaggingClassifier()
score: [1. 1. 1. 1. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 1.
 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 0. 1. 1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1.]
mean_value: 0.796875
------------------------------------------------------------
AdaBoostClassifier()
score: [1. 1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 1. 1. 1.]
mean_value: 0.8125
------------------------------------------------------------
LogisticRegression()
score: [1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 1. 1. 1. 1. 0.
 0. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 1. 0. 1. 0. 1.]
mean_value: 0.734375
------------------------------------------------------------
SVC()
score: [1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0.
 0. 0. 1. 0. 0. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1.
 1. 1. 0. 0. 1. 1. 0. 1. 0. 1. 1. 1. 0. 0. 1. 1.]
mean_value: 0.6875
------------------------------------------------------------
KNeighborsClassifier()
score: [0. 1. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0.
 0. 0. 0. 0. 0. 1. 0. 1. 1. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 1. 1. 1. 0. 1.
 1. 1. 0. 1. 1. 1. 0. 1. 0. 1. 1. 1. 0. 0. 1. 1.]
mean_value: 0.578125
------------------------------------------------------------
GaussianNB()
score: [1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 0.
 0. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 1. 0. 0.]
mean_value: 0.703125
------------------------------------------------------------

DecisionTreeClassifier

models =DecisionTreeClassifier(max_depth=8, min_samples_split=6, splitter='random')
models.fit(x_train,y_train)

DecisionTreeClassifier(max_depth=8, min_samples_split=6, splitter='random')

pred = models.predict(x_test)

print(classification_report(y_test, pred))

              precision    recall  f1-score   support

           0       0.67      0.86      0.75         7
           1       0.75      0.50      0.60         6

    accuracy                           0.69        13
   macro avg       0.71      0.68      0.68        13
weighted avg       0.71      0.69      0.68        13

RandomForestClassifier

models =RandomForestClassifier(max_features='sqrt', n_jobs=-1, random_state=42)
models.fit(x_train,y_train)

RandomForestClassifier(max_features='sqrt', n_jobs=-1, random_state=42)

pred = models.predict(x_test)

print(classification_report(y_test,pred))

              precision    recall  f1-score   support

           0       0.70      1.00      0.82         7
           1       1.00      0.50      0.67         6

    accuracy                           0.77        13
   macro avg       0.85      0.75      0.75        13
weighted avg       0.84      0.77      0.75        13

BaggingClassifier

models=BaggingClassifier(max_samples=8, n_estimators=40)
models.fit(x_train,y_train)

BaggingClassifier(max_samples=8, n_estimators=40)

pred = models.predict(x_test)

print(classification_report(y_test,pred))

              precision    recall  f1-score   support

           0       0.78      1.00      0.88         7
           1       1.00      0.67      0.80         6

    accuracy                           0.85        13
   macro avg       0.89      0.83      0.84        13
weighted avg       0.88      0.85      0.84        13

KNeighborsClassifier

model =KNeighborsClassifier(algorithm='ball_tree', p=3)

model.fit(x_train,y_train)

KNeighborsClassifier(algorithm='ball_tree', p=3)

pred= model.predict(x_test)

print(classification_report(y_test,pred))

              precision    recall  f1-score   support

           0       0.50      0.29      0.36         7
           1       0.44      0.67      0.53         6

    accuracy                           0.46        13
   macro avg       0.47      0.48      0.45        13
weighted avg       0.47      0.46      0.44        13

models = [
    
    ('DecisionTreeClassifier',DecisionTreeClassifier()),
    ('RandomForestClassifier', RandomForestClassifier()),
    ('GradientBoostingClassifier', GradientBoostingClassifier()),
    ('BaggingClassifier',BaggingClassifier()),
    ('AdaBoostClassifier',AdaBoostClassifier()),
    ('LogisticRegression',LogisticRegression()),
    ('SVC',SVC()),
    ('KNeighborsClassifier',KNeighborsClassifier()),
    ('GaussianNB',GaussianNB())
]

para_grids= {
    'DecisionTreeClassifier':{
        'criterion':['gini','entropy','log_loss'],
        'splitter':['best','random'],
        'max_depth':[6,8,10,12],
        'min_samples_split':[2,3,4,5,6]
    },
    'RandomForestClassifier':{
       'n_estimators':[10,40,50,60,70,100],
        'criterion':['gini','entropy','log_loss'],
        'max_depth':[6,8,10,12],
        'min_samples_split':[2,3,4,5,6]
    },
    'GradientBoostingClassifier':{
        'n_estimators':[10,40,50,60,70,100],
        'min_samples_split':[2,3,4,5,6]
    },
    'BaggingClassifier':{
        'n_estimators':[10,40,50,60,70,100],
        'max_samples':[6,8,10,12]
    },
    'AdaBoostClassifier':{
        'n_estimators':[10,40,50,60,70,100]
    },
    'LogisticRegression':{
    },
    'SVC':{
    },
    'KNeighborsClassifier':{
        'n_neighbors':[2,3,4,5],
        'weights':['distance','uniform'],
        'algorithm':['ball_tree','kd_tree','brute'],
        'p':[2,3,4,5,6]
    },
    'GaussianNB':{}
}
 

GridSearchCV

for model_name,model_instance in models:
    print(f'Model: {model_name}')
    
    Gscv = GridSearchCV(estimator=model_instance,param_grid=para_grids[model_name],cv=5,n_jobs=-1)
    
    # Fit the model
    Gscv.fit(x_train,y_train)
    
    
    # Make predictions
    pred = Gscv.predict(x_test)
    
    # Print classification report
    # Get the best estimator
    print(Gscv.best_estimator_)
    print(classification_report(y_test, pred))
    print('------'*10)

Model: DecisionTreeClassifier
DecisionTreeClassifier(max_depth=12, min_samples_split=5)
              precision    recall  f1-score   support

           0       0.50      0.71      0.59         7
           1       0.33      0.17      0.22         6

    accuracy                           0.46        13
   macro avg       0.42      0.44      0.41        13
weighted avg       0.42      0.46      0.42        13

------------------------------------------------------------
Model: RandomForestClassifier
RandomForestClassifier(max_depth=6, min_samples_split=6, n_estimators=50)
              precision    recall  f1-score   support

           0       0.70      1.00      0.82         7
           1       1.00      0.50      0.67         6

    accuracy                           0.77        13
   macro avg       0.85      0.75      0.75        13
weighted avg       0.84      0.77      0.75        13

------------------------------------------------------------
Model: GradientBoostingClassifier
GradientBoostingClassifier(min_samples_split=6, n_estimators=40)
              precision    recall  f1-score   support

           0       0.62      0.71      0.67         7
           1       0.60      0.50      0.55         6

    accuracy                           0.62        13
   macro avg       0.61      0.61      0.61        13
weighted avg       0.61      0.62      0.61        13

------------------------------------------------------------
Model: BaggingClassifier
BaggingClassifier(max_samples=12, n_estimators=70)
              precision    recall  f1-score   support

           0       0.70      1.00      0.82         7
           1       1.00      0.50      0.67         6

    accuracy                           0.77        13
   macro avg       0.85      0.75      0.75        13
weighted avg       0.84      0.77      0.75        13

------------------------------------------------------------
Model: AdaBoostClassifier
AdaBoostClassifier(n_estimators=60)
              precision    recall  f1-score   support

           0       0.75      0.86      0.80         7
           1       0.80      0.67      0.73         6

    accuracy                           0.77        13
   macro avg       0.78      0.76      0.76        13
weighted avg       0.77      0.77      0.77        13

------------------------------------------------------------
Model: LogisticRegression
LogisticRegression()
              precision    recall  f1-score   support

           0       0.83      0.71      0.77         7
           1       0.71      0.83      0.77         6

    accuracy                           0.77        13
   macro avg       0.77      0.77      0.77        13
weighted avg       0.78      0.77      0.77        13

------------------------------------------------------------
Model: SVC
SVC()
              precision    recall  f1-score   support

           0       0.71      0.71      0.71         7
           1       0.67      0.67      0.67         6

    accuracy                           0.69        13
   macro avg       0.69      0.69      0.69        13
weighted avg       0.69      0.69      0.69        13

------------------------------------------------------------
Model: KNeighborsClassifier
KNeighborsClassifier(algorithm='ball_tree', p=4)
              precision    recall  f1-score   support

           0       0.50      0.29      0.36         7
           1       0.44      0.67      0.53         6

    accuracy                           0.46        13
   macro avg       0.47      0.48      0.45        13
weighted avg       0.47      0.46      0.44        13

------------------------------------------------------------
Model: GaussianNB
GaussianNB()
              precision    recall  f1-score   support

           0       0.75      0.86      0.80         7
           1       0.80      0.67      0.73         6

    accuracy                           0.77        13
   macro avg       0.78      0.76      0.76        13
weighted avg       0.77      0.77      0.77        13

------------------------------------------------------------

RandomizedSearchCV

for model_name,model_instance in models:
    print(f'Model: {model_name}')
    
    rscv = RandomizedSearchCV(estimator=model_instance,param_distributions=para_grids[model_name],cv=5,n_jobs=-1)
    
    # Fit the model
    rscv.fit(x_train,y_train)
    
    
    # Make predictions
    pred = rscv.predict(x_test)
    
    # Print classification report
    # Get the best estimator
    print(rscv.best_estimator_)
    print(classification_report(y_test, pred))
    print('------'*10)
    

Model: DecisionTreeClassifier
DecisionTreeClassifier(max_depth=12, min_samples_split=3)
              precision    recall  f1-score   support

           0       0.57      0.57      0.57         7
           1       0.50      0.50      0.50         6

    accuracy                           0.54        13
   macro avg       0.54      0.54      0.54        13
weighted avg       0.54      0.54      0.54        13

------------------------------------------------------------
Model: RandomForestClassifier
RandomForestClassifier(max_depth=10, min_samples_split=3)
              precision    recall  f1-score   support

           0       0.60      0.86      0.71         7
           1       0.67      0.33      0.44         6

    accuracy                           0.62        13
   macro avg       0.63      0.60      0.58        13
weighted avg       0.63      0.62      0.59        13

------------------------------------------------------------
Model: GradientBoostingClassifier
GradientBoostingClassifier(min_samples_split=6, n_estimators=10)
              precision    recall  f1-score   support

           0       0.62      0.71      0.67         7
           1       0.60      0.50      0.55         6

    accuracy                           0.62        13
   macro avg       0.61      0.61      0.61        13
weighted avg       0.61      0.62      0.61        13

------------------------------------------------------------
Model: BaggingClassifier
BaggingClassifier(max_samples=10, n_estimators=100)
              precision    recall  f1-score   support

           0       0.67      0.86      0.75         7
           1       0.75      0.50      0.60         6

    accuracy                           0.69        13
   macro avg       0.71      0.68      0.68        13
weighted avg       0.71      0.69      0.68        13

------------------------------------------------------------
Model: AdaBoostClassifier
AdaBoostClassifier(n_estimators=60)
              precision    recall  f1-score   support

           0       0.75      0.86      0.80         7
           1       0.80      0.67      0.73         6

    accuracy                           0.77        13
   macro avg       0.78      0.76      0.76        13
weighted avg       0.77      0.77      0.77        13

------------------------------------------------------------
Model: LogisticRegression
LogisticRegression()
              precision    recall  f1-score   support

           0       0.83      0.71      0.77         7
           1       0.71      0.83      0.77         6

    accuracy                           0.77        13
   macro avg       0.77      0.77      0.77        13
weighted avg       0.78      0.77      0.77        13

------------------------------------------------------------
Model: SVC
SVC()
              precision    recall  f1-score   support

           0       0.71      0.71      0.71         7
           1       0.67      0.67      0.67         6

    accuracy                           0.69        13
   macro avg       0.69      0.69      0.69        13
weighted avg       0.69      0.69      0.69        13

------------------------------------------------------------
Model: KNeighborsClassifier
KNeighborsClassifier(algorithm='kd_tree', p=6)
              precision    recall  f1-score   support

           0       0.50      0.29      0.36         7
           1       0.44      0.67      0.53         6

    accuracy                           0.46        13
   macro avg       0.47      0.48      0.45        13
weighted avg       0.47      0.46      0.44        13

------------------------------------------------------------
Model: GaussianNB
GaussianNB()
              precision    recall  f1-score   support

           0       0.75      0.86      0.80         7
           1       0.80      0.67      0.73         6

    accuracy                           0.77        13
   macro avg       0.78      0.76      0.76        13
weighted avg       0.77      0.77      0.77        13

------------------------------------------------------------

pip install tensorflow

Requirement already satisfied: tensorflow in c:\users\vinayaka\anaconda3\lib\site-packages (2.16.1)
Requirement already satisfied: tensorflow-intel==2.16.1 in c:\users\vinayaka\anaconda3\lib\site-packages (from tensorflow) (2.16.1)
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Requirement already satisfied: numpy<2.0.0,>=1.23.5 in c:\users\vinayaka\anaconda3\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.26.4)
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Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\users\vinayaka\anaconda3\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.31.0)
Requirement already satisfied: absl-py>=1.0.0 in c:\users\vinayaka\anaconda3\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (2.1.0)
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Requirement already satisfied: h5py>=3.10.0 in c:\users\vinayaka\anaconda3\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (3.11.0)
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Requirement already satisfied: astunparse>=1.6.0 in c:\users\vinayaka\anaconda3\lib\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.6.3)
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Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\users\vinayaka\anaconda3\lib\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.16.1->tensorflow) (1.26.11)
Requirement already satisfied: werkzeug>=1.0.1 in c:\users\vinayaka\anaconda3\lib\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (2.0.3)
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Requirement already satisfied: markdown>=2.6.8 in c:\users\vinayaka\anaconda3\lib\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.3.4)
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Requirement already satisfied: markdown-it-py>=2.2.0 in c:\users\vinayaka\anaconda3\lib\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (3.0.0)
Requirement already satisfied: mdurl~=0.1 in c:\users\vinayaka\anaconda3\lib\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.1.2)
Note: you may need to restart the kernel to use updated packages.

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(36,input_shape= (x_train.shape[1],),activation= 'relu'),
    tf.keras.layers.Dense(10,activation='elu'),
    tf.keras.layers.Dense(1,activation='sigmoid')
])

model.compile(optimizer='Adam',loss='binary_crossentropy',metrics=['accuracy'])

history = model.fit(x_train,y_train,batch_size=6,epochs=100,verbose=0)

his_df = pd.DataFrame(history.history)

his_df.head()

	accuracy	loss
0	0.490196	110.409637
1	0.411765	43.312870
2	0.411765	20.571758
3	0.392157	18.936363
4	0.352941	10.823765

his_df.plot()

<AxesSubplot:>

loss,accuracy = model.evaluate(x_test,y_test)

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step - accuracy: 0.8462 - loss: 0.3102

accuracy

0.8461538553237915